**Adaptation and Neuronal Network in Visual Cortex**

Lyes Bachatene, Vishal Bharmauria and Stéphane Molotchnikoff

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/46011

## **1. Introduction**

322 Visual Cortex – Current Status and Perspectives

30-40.

[148] Burke SN, Barnes CA (2006) Neural plasticity in the ageing brain*.* Nat Rev Neurosci 7:

[149] Scali M, Baroncelli L, Cenni MC, Sale A, Maffei L A rich environmental experience

[150] Kempermann G, Kuhn HG, Gage FH (1998) Experience-induced neurogenesis in the

reactivates visual cortex plasticity in aged rats*.* Exp Gerontol 47: 337-341.

senescent dentate gyrus*.* J Neurosci 18: 3206-3212.

Complex mechanisms from retina to different visual areas allow us to read these lines. The visual system is inevitable for the way we interact with our surroundings as majority of our impressions, memories, feelings are bound to the visual perception. Millions of cortical neurons are implicated and programmed specifically to frame this incredible interface (perception) for us to interact with the world. Neurons in the visual cortex respond essentially to the variations in luminance occurring within their receptive fields, where each neuron fires maximally by acting as a filter for stimulus features such as orientation, motion, direction and velocity, with an appropriate combination of these properties [1-5].

The seminal work of Hubel and Wiesel on the visual cortex of cat [1, 2, 6-8], has been instrumental in establishing the anatomical and physiological aspects of the visual cortex. Many studies by various investigators on the visual cortex of different animals, thereafter, have been phenomenal in understanding the brain in general and the vision in particular; yet, neuronal mechanisms involved in processing of stimuli still elude our complete understanding of cortical functioning. These findings have been crucial in unravelling the organization of the visual cortex. The visual cortex reorganises itself in the postnatal development, within a specific period called 'the critical period' [9], which is a period characterised with pronounced brain plasticity. In recent years, the focus of the research has been to comprehend the 'reorganization' of neuronal framework, especially after the so called 'critical period' [10-12] in response to various conditions and its ability to adapt accordingly. This amazing tendency of brain to change its neuronal connections and properties is termed 'plasticity' [13]. Two common approaches to study the reorganization of visual cortex are frequently applied: deprivation and induced adaptation. Deprivation refers to the removal of sensory inputs, whereas induced adaptation refers to the forceful application of a sensory input. Consequently, neurons communicate dynamically with each

© 2012 Molotchnikoff et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

other in a specific way self-assembling, auto-calibrating, memorizing and adapting to different stimuli properties, thus responding accordingly to several experiences [14-16].

Adaptation and Neuronal Network in Visual Cortex 325

Therefore, all the neurons in these brain regions form a topographical map of the visual field

From the LGN, axons are organized into thalamocortical fibres forming the optic radiations. These optic radiations project onto the cortex in specialized visual areas. The distribution of fibres in the cortex can reproduce the visual field on the cortical layer, and the stimulation of a small cortical area leads to the appearance of bright spots called 'phosphenes' [25] in a specific location of the visual field. Visual areas begin in the occipital lobe, and the primary visual cortex or area 17 is the main entrance to cortex for thalamic relay cells [24]. The primary visual cortex is organized into functional modules. Neurons with similar receptive fields are organized into columns [26, 27]. Visual neurons have other fundamental properties, such as the direction selectivity of cells in the layer IVβ, and the selectivity for speed [3, 6]. There is another system of alternating columns, which corresponds to the separation of afferents from both eyes. These are the ocular dominance columns. The ocular dominance columns represent bands of cortical tissue alternately occupied by afferents from the left eye or right eye [28-30]. These bands are particularly pronounced at the cortical layer IV, which receives the afferent endings of the lateral geniculate nucleus. Thus the visual cortex is organized into functional maps of orientation, spatial frequency, ocular dominance,

temporal frequency which are interrelated to each other [31-33].

**Figure 1.** Parallel organization of the visual system

from its projection onto the retina.

The aim of this chapter is to primarily focus on how the linkage between cells changes following plastic modifications of cortical neuronal properties, that is, how the reorganization of the cortical network is modulated following adaptation-induced plasticity, as it is inferred by cross-correlating the action potentials of the neurons in the primary visual cortex. We begin with the general architecture of visual cortex (particularly cat visual cortex), followed by a brief introduction to plasticity and adaptation. Then, we cite an example of modification of the neuronal connections before and after adaptation as revealed by cross-correlation method. Based on this example, we propose a model for changing functional connections prior and post adaptation and conclude with how neurons change their functional relationships when forcefully adapted to a non-optimal stimulus.

## **2. Visual system: Organization**

## **2.1. Introduction**

Visual area constitutes about 25 % of the cortex in humans with approximately 5 billion neurons. The study of the visual cortex has revealed many of these visual regions such as V1, V2, V3, V4 and MT on the basis of their anatomical architecture, topography and physiological properties [17, 18]. These regions are involved in processing of multitude of informations (shape, orientation, color, movement, size etc) resulting from the visual pathways, thus making up an image applied to retina.

The cortical area of higher mammals such as cats, monkeys and humans is generally divided into modules of selectivity (e.g. the visual cortex is divided into areas of selectivity called orientation columns). Several characteristics of the visual system of mammals appear to be common to many species [19, 20], though the neurons are distributed in a salt and pepper fashion in the visual cortex of lower animals such as rats and mice, lacking the orientation domains [21, 22]. Research on animal models is used on a large scale to study and investigate the structure and function of the visual system. Monkeys, cats, and mice are commonly used in neurophysiological experiments for understanding cortical mechanisms in general and visual pathways in particular [23].

## **2.2. From retina to visual areas**

Visual perception begins in the retina where the received light is transformed into electrical signal by a biochemical cascade produced in the rods and cones. The retinal ganglion cells relay the message to the lateral geniculate body (LGN) which consists of six layers [24]. Each layer receives information from the retinal hemi-field of one eye. The axon terminals of ganglion cells which project on each layer form a precise retinotopic map. This retinotopy denotes the spatial organization of neuronal responses to visual stimuli. Indeed, in many parts of the brain, neurons that respond to stimulation from a given portion of the visual field are located right next to the neurons whose receptive fields cover adjacent portions. Therefore, all the neurons in these brain regions form a topographical map of the visual field from its projection onto the retina.

324 Visual Cortex – Current Status and Perspectives

**2. Visual system: Organization** 

pathways, thus making up an image applied to retina.

in general and visual pathways in particular [23].

**2.2. From retina to visual areas** 

**2.1. Introduction** 

other in a specific way self-assembling, auto-calibrating, memorizing and adapting to different stimuli properties, thus responding accordingly to several experiences [14-16].

The aim of this chapter is to primarily focus on how the linkage between cells changes following plastic modifications of cortical neuronal properties, that is, how the reorganization of the cortical network is modulated following adaptation-induced plasticity, as it is inferred by cross-correlating the action potentials of the neurons in the primary visual cortex. We begin with the general architecture of visual cortex (particularly cat visual cortex), followed by a brief introduction to plasticity and adaptation. Then, we cite an example of modification of the neuronal connections before and after adaptation as revealed by cross-correlation method. Based on this example, we propose a model for changing functional connections prior and post adaptation and conclude with how neurons change

Visual area constitutes about 25 % of the cortex in humans with approximately 5 billion neurons. The study of the visual cortex has revealed many of these visual regions such as V1, V2, V3, V4 and MT on the basis of their anatomical architecture, topography and physiological properties [17, 18]. These regions are involved in processing of multitude of informations (shape, orientation, color, movement, size etc) resulting from the visual

The cortical area of higher mammals such as cats, monkeys and humans is generally divided into modules of selectivity (e.g. the visual cortex is divided into areas of selectivity called orientation columns). Several characteristics of the visual system of mammals appear to be common to many species [19, 20], though the neurons are distributed in a salt and pepper fashion in the visual cortex of lower animals such as rats and mice, lacking the orientation domains [21, 22]. Research on animal models is used on a large scale to study and investigate the structure and function of the visual system. Monkeys, cats, and mice are commonly used in neurophysiological experiments for understanding cortical mechanisms

Visual perception begins in the retina where the received light is transformed into electrical signal by a biochemical cascade produced in the rods and cones. The retinal ganglion cells relay the message to the lateral geniculate body (LGN) which consists of six layers [24]. Each layer receives information from the retinal hemi-field of one eye. The axon terminals of ganglion cells which project on each layer form a precise retinotopic map. This retinotopy denotes the spatial organization of neuronal responses to visual stimuli. Indeed, in many parts of the brain, neurons that respond to stimulation from a given portion of the visual field are located right next to the neurons whose receptive fields cover adjacent portions.

their functional relationships when forcefully adapted to a non-optimal stimulus.

From the LGN, axons are organized into thalamocortical fibres forming the optic radiations. These optic radiations project onto the cortex in specialized visual areas. The distribution of fibres in the cortex can reproduce the visual field on the cortical layer, and the stimulation of a small cortical area leads to the appearance of bright spots called 'phosphenes' [25] in a specific location of the visual field. Visual areas begin in the occipital lobe, and the primary visual cortex or area 17 is the main entrance to cortex for thalamic relay cells [24]. The primary visual cortex is organized into functional modules. Neurons with similar receptive fields are organized into columns [26, 27]. Visual neurons have other fundamental properties, such as the direction selectivity of cells in the layer IVβ, and the selectivity for speed [3, 6]. There is another system of alternating columns, which corresponds to the separation of afferents from both eyes. These are the ocular dominance columns. The ocular dominance columns represent bands of cortical tissue alternately occupied by afferents from the left eye or right eye [28-30]. These bands are particularly pronounced at the cortical layer IV, which receives the afferent endings of the lateral geniculate nucleus. Thus the visual cortex is organized into functional maps of orientation, spatial frequency, ocular dominance, temporal frequency which are interrelated to each other [31-33].

**Figure 1.** Parallel organization of the visual system

Many findings have led to the discovery of thirty different cortical areas that contribute to visual perception. The primary areas (V1) and secondary areas (V2) are surrounded by many other tertiary or associative visual areas such as V3, V4, V5 (or MT) involved in processing various attributes of trigger features [18, 34]. Areas V3 and V3A are selective to the form of stimuli [35], and neurons of area V4 are selective to colors [36]. Area V5 or MT (middle temporal) is an area where majority of cells are sensitive to motion and direction, and none of which are selective to color [37].

Adaptation and Neuronal Network in Visual Cortex 327

**Figure 2.** Organization of the primary visual cortex

**3.2. Receptive fields** 

**Figure 3.** Spike waveforms for putative interneurons (a) and putative pyramidal cells (b)

orientation, direction, length, width, and motion [3, 6, 49].

Visual information from the LGN cells firstly projects onto the stellate interneurons in layer IV, which have concentric receptive fields similar to those of LGN neurons [24, 48]. Neurons of layer IV project vertically onto other cortical layers. In layers II / III, cortical cells exhibit a radical transformation of the receptive field organization, where cells respond preferentially to stimuli with properties such as a bar or an edge that has specific characteristics namely

Cells in these layers are classified into simple, complex and hypercomplex cells based on their dark or light-edge properties [6]. Simple cell is a cell which has an ON-OFF sub field,

Moreover, the parallel organization of visual system is involved in the establishment of two major visual pathways: Ventral and dorsal pathways which are indispensable for the object recognition [38, 39]. Figure 1 illustrates the parallel organization of visual system, two major pathways: Green part corresponds to the ventral pathway in the cortex ending in the temporal lobe [38, 40]. It is involved in the processing of information on the characteristics of the objects (shapes, colours, materials), that is, object recognition including faces. Orange part corresponds to the dorsal pathway in the cortex ending in the parietal lobe [38, 40]. This path is associated with spatial vision (action / location) of objects, and is involved in processing of action in space.

## **3. Neuron types in primary visual cortex**

## **3.1. Pyramidal cells and interneurons**

The grey matter in the primary visual cortex is divided into six layers namely I, II, III, IV, V, VI (Figure 2) which comprise of different types of neurons [41, 42]. Two types of neurons are mainly observed: pyramidal cells and interneurons which can be physiologically separated and are the focus of interest in this chapter, that is, how they modify their properties and change linkage with each other post-adaptation. Pyramidal cells are excitatory neurons projecting onto other brain regions [43, 44] whereas stellate cells which are the recipient cells from the relay cells of the LGN correspond to the local excitatory interneurons [45]. In addition, there are interneurons that are inhibitory in nature [45]. Figure 2 illustrates the layers and cell types in primary visual cortex. Each layer has specific cell types and connectivity in primary visual cortex. Layer IV contains many stellate cells, small neurons with dendrites arranged radially around the cell body. Pyramidal cells are found in layers II-III, V and VI and are the only type of neurons that send axons outside the cortex. These neurons exhibit two levels of their dendritic extension: basal level close to the cell body and relatively long apical dendritic branches extending sometimes over the entire thickness of the cortex.

Classically, spike waveforms allow cells' distinction into two functional cell-groups, that is, excitatory pyramidal cells and inhibitory interneurons [11, 46, 47]. Figure 3 illustrates a typical example of cells distinguished based on their waveforms: fast spike and regular spike. Figure 3a corresponds to a fast spike with steeper ascending slope of the action potential and represents the putative interneuron [11], whereas Figure 3b corresponds to a regular spike which exhibits a slower ascending slope and represents the putative pyramidal cell [11].

**Figure 2.** Organization of the primary visual cortex

**Figure 3.** Spike waveforms for putative interneurons (a) and putative pyramidal cells (b)

## **3.2. Receptive fields**

326 Visual Cortex – Current Status and Perspectives

processing of action in space.

the cortex.

pyramidal cell [11].

and none of which are selective to color [37].

**3. Neuron types in primary visual cortex** 

**3.1. Pyramidal cells and interneurons** 

Many findings have led to the discovery of thirty different cortical areas that contribute to visual perception. The primary areas (V1) and secondary areas (V2) are surrounded by many other tertiary or associative visual areas such as V3, V4, V5 (or MT) involved in processing various attributes of trigger features [18, 34]. Areas V3 and V3A are selective to the form of stimuli [35], and neurons of area V4 are selective to colors [36]. Area V5 or MT (middle temporal) is an area where majority of cells are sensitive to motion and direction,

Moreover, the parallel organization of visual system is involved in the establishment of two major visual pathways: Ventral and dorsal pathways which are indispensable for the object recognition [38, 39]. Figure 1 illustrates the parallel organization of visual system, two major pathways: Green part corresponds to the ventral pathway in the cortex ending in the temporal lobe [38, 40]. It is involved in the processing of information on the characteristics of the objects (shapes, colours, materials), that is, object recognition including faces. Orange part corresponds to the dorsal pathway in the cortex ending in the parietal lobe [38, 40]. This path is associated with spatial vision (action / location) of objects, and is involved in

The grey matter in the primary visual cortex is divided into six layers namely I, II, III, IV, V, VI (Figure 2) which comprise of different types of neurons [41, 42]. Two types of neurons are mainly observed: pyramidal cells and interneurons which can be physiologically separated and are the focus of interest in this chapter, that is, how they modify their properties and change linkage with each other post-adaptation. Pyramidal cells are excitatory neurons projecting onto other brain regions [43, 44] whereas stellate cells which are the recipient cells from the relay cells of the LGN correspond to the local excitatory interneurons [45]. In addition, there are interneurons that are inhibitory in nature [45]. Figure 2 illustrates the layers and cell types in primary visual cortex. Each layer has specific cell types and connectivity in primary visual cortex. Layer IV contains many stellate cells, small neurons with dendrites arranged radially around the cell body. Pyramidal cells are found in layers II-III, V and VI and are the only type of neurons that send axons outside the cortex. These neurons exhibit two levels of their dendritic extension: basal level close to the cell body and relatively long apical dendritic branches extending sometimes over the entire thickness of

Classically, spike waveforms allow cells' distinction into two functional cell-groups, that is, excitatory pyramidal cells and inhibitory interneurons [11, 46, 47]. Figure 3 illustrates a typical example of cells distinguished based on their waveforms: fast spike and regular spike. Figure 3a corresponds to a fast spike with steeper ascending slope of the action potential and represents the putative interneuron [11], whereas Figure 3b corresponds to a regular spike which exhibits a slower ascending slope and represents the putative Visual information from the LGN cells firstly projects onto the stellate interneurons in layer IV, which have concentric receptive fields similar to those of LGN neurons [24, 48]. Neurons of layer IV project vertically onto other cortical layers. In layers II / III, cortical cells exhibit a radical transformation of the receptive field organization, where cells respond preferentially to stimuli with properties such as a bar or an edge that has specific characteristics namely orientation, direction, length, width, and motion [3, 6, 49].

Cells in these layers are classified into simple, complex and hypercomplex cells based on their dark or light-edge properties [6]. Simple cell is a cell which has an ON-OFF sub field,

that is, it responds to ON or OFF stimuli in the receptive field and has adjacent excitatory and inhibitory areas [6]. Complex neurons have receptive fields larger than those of simple neurons. They are also selective for orientation, but the precise position of the stimulus within the receptive field is less critical because they have no defined ON or OFF sub-areas. That is why a movement of the stimulus through the receptive field is a potent stimulus for some complex neurons [6, 50, 51]. A complex cell does not have adjacent excitatory and inhibitory areas in the receptive field and responds to whole of the receptive field regardless of the exactitude of the stimulus area, though complex cells can be direction-specific [6]. A hypercomplex cell appears to result, when the axons of complex neurons with different orientations converge on it. A hypercomplex cell is selective to lines of a defined length, and if the stimulus exceeds this length the response is diminished due to inhibitory extremities in addition to antagonistic flanks [6].

Adaptation and Neuronal Network in Visual Cortex 329

visual cortex neurons in cats, e.g. Long adaptation leads to the shift of orientation tuning towards attractive direction [61]. In a similar fashion, repetitive adaptation to a nonpreferred spatial frequency reveals the spatial frequency tuning shifts in cat visual cortex [49]. In general, imposing a particular stimulus induces instructive process to modify neuronal properties, for example, when in the visual cortex of awake mice a single orientation grating stimulus is repeatedly presented; it leads to augmentation of responses evoked exclusively by testing stimulus, that is, the experience led to enhancement of response [66]. In experiments where animals are anaesthetized (e.g. a cat in the experiment described later on) the shifts of peaks of tuning curves following adaptation (described in figure 4) are not attributed to attention modulations. Consequently, these shifts result from basic neuronal processes outside the frame of attentional processes that might impact

Adaptation studies in recent years have presented a more complex picture where prolonged exposure to a non-preferred orientation has shown modifications in neurons' preferred orientations. After adaptation to a non preferred orientation, obtained tuning curve for the new preferred orientation (after adaptation) can shift in two directions relative to the original preferred orientation: attractive or repulsive [11, 61, 63, 64]. An attractive shift is a shift of the tuning curve towards the adapting orientation. On the other hand; a repulsive shift is a shift of the tuning curve in the opposite side of the adapting orientation. Figure 4 illustrates types of shifts post-adaptation. Figure 4a corresponds to an attractive shift, in which blue tuning curve represents control optimal orientation (before adaptation), and red tuning curve represents new optimal orientation (after adaptation).The tuning curve shifted towards the adapting orientation. Figure 4b corresponds to the repulsive shift, in which blue tuning curve represents control optimal orientation (before adaptation), and red tuning curve represents new optimal orientation (after adaptation). The tuning curve shifted away from the adapting orientation.

Red arrows depict adapting orientation (non-preferred orientation in control).

response magnitudes.

**Figure 4.** Types of shifts post-adaptation

Neurons in primary visual cortex are connected laterally and vertically to each other. Lateral or horizontal connections are specified as long range connections between neurons preferring similar stimulus features [52, 53] which are functionally connected to each other at large distance [54, 55]. Vertical connections are specified as inputs to layers II and III from layer IV of the visual cortex [56, 57] which receives its inputs from LGN [58]. From layers II and III, the connections descend to layers V and VI [57].

Cats have a high performance visual system close to that of primates, making it a very coveted subject for researches to reveal the functional aspects of this complex system [59, 60]. Approaches to study the visual system are based on functional electrophysiology, where animals are anaesthetized and paralyzed for electrophysiological recordings by lowering microelectrodes into regions of interest within the visual area and visually stimulating the neurons [6, 11, 49, 61].

## **4. Plasticity and adaptation in the visual cortex**

Neurons in the mammalian visual cortex are tuned to respond to visual stimuli such as contour orientation, motion, direction, and speed [3, 6, 59]. Preference for orientation in orientation columns is considered relatively stable in the primary visual cortex (V1) as an emergent property that is established early in the life, following the so-called critical period [9].

Studies from various laboratories have shown that in a fully mature brain, neuronal network restructures itself beyond the postnatal critical period that follows birth [11, 49, 61- 65]. Recent investigations revealed the ability of visual neurons to respond to different stimuli conditions (deprivation or imposition) by changing their optimal properties acquired after birth. This adaptation of neurons for visual perception suggests the existence of neuronal plasticity in adults, hence a mature brain.

Adaptation-induced-plasticity of orientation in primary visual cortex is characterized by authors as the ability of cortical neurons to change their preferred orientation following a long [11, 61, 63] or short [62, 65] exposure to a non-preferred orientation for the primary visual cortex neurons in cats, e.g. Long adaptation leads to the shift of orientation tuning towards attractive direction [61]. In a similar fashion, repetitive adaptation to a nonpreferred spatial frequency reveals the spatial frequency tuning shifts in cat visual cortex [49]. In general, imposing a particular stimulus induces instructive process to modify neuronal properties, for example, when in the visual cortex of awake mice a single orientation grating stimulus is repeatedly presented; it leads to augmentation of responses evoked exclusively by testing stimulus, that is, the experience led to enhancement of response [66]. In experiments where animals are anaesthetized (e.g. a cat in the experiment described later on) the shifts of peaks of tuning curves following adaptation (described in figure 4) are not attributed to attention modulations. Consequently, these shifts result from basic neuronal processes outside the frame of attentional processes that might impact response magnitudes.

Adaptation studies in recent years have presented a more complex picture where prolonged exposure to a non-preferred orientation has shown modifications in neurons' preferred orientations. After adaptation to a non preferred orientation, obtained tuning curve for the new preferred orientation (after adaptation) can shift in two directions relative to the original preferred orientation: attractive or repulsive [11, 61, 63, 64]. An attractive shift is a shift of the tuning curve towards the adapting orientation. On the other hand; a repulsive shift is a shift of the tuning curve in the opposite side of the adapting orientation. Figure 4 illustrates types of shifts post-adaptation. Figure 4a corresponds to an attractive shift, in which blue tuning curve represents control optimal orientation (before adaptation), and red tuning curve represents new optimal orientation (after adaptation).The tuning curve shifted towards the adapting orientation. Figure 4b corresponds to the repulsive shift, in which blue tuning curve represents control optimal orientation (before adaptation), and red tuning curve represents new optimal orientation (after adaptation). The tuning curve shifted away from the adapting orientation. Red arrows depict adapting orientation (non-preferred orientation in control).

**Figure 4.** Types of shifts post-adaptation

328 Visual Cortex – Current Status and Perspectives

in addition to antagonistic flanks [6].

stimulating the neurons [6, 11, 49, 61].

[9].

and III, the connections descend to layers V and VI [57].

**4. Plasticity and adaptation in the visual cortex** 

of neuronal plasticity in adults, hence a mature brain.

that is, it responds to ON or OFF stimuli in the receptive field and has adjacent excitatory and inhibitory areas [6]. Complex neurons have receptive fields larger than those of simple neurons. They are also selective for orientation, but the precise position of the stimulus within the receptive field is less critical because they have no defined ON or OFF sub-areas. That is why a movement of the stimulus through the receptive field is a potent stimulus for some complex neurons [6, 50, 51]. A complex cell does not have adjacent excitatory and inhibitory areas in the receptive field and responds to whole of the receptive field regardless of the exactitude of the stimulus area, though complex cells can be direction-specific [6]. A hypercomplex cell appears to result, when the axons of complex neurons with different orientations converge on it. A hypercomplex cell is selective to lines of a defined length, and if the stimulus exceeds this length the response is diminished due to inhibitory extremities

Neurons in primary visual cortex are connected laterally and vertically to each other. Lateral or horizontal connections are specified as long range connections between neurons preferring similar stimulus features [52, 53] which are functionally connected to each other at large distance [54, 55]. Vertical connections are specified as inputs to layers II and III from layer IV of the visual cortex [56, 57] which receives its inputs from LGN [58]. From layers II

Cats have a high performance visual system close to that of primates, making it a very coveted subject for researches to reveal the functional aspects of this complex system [59, 60]. Approaches to study the visual system are based on functional electrophysiology, where animals are anaesthetized and paralyzed for electrophysiological recordings by lowering microelectrodes into regions of interest within the visual area and visually

Neurons in the mammalian visual cortex are tuned to respond to visual stimuli such as contour orientation, motion, direction, and speed [3, 6, 59]. Preference for orientation in orientation columns is considered relatively stable in the primary visual cortex (V1) as an emergent property that is established early in the life, following the so-called critical period

Studies from various laboratories have shown that in a fully mature brain, neuronal network restructures itself beyond the postnatal critical period that follows birth [11, 49, 61- 65]. Recent investigations revealed the ability of visual neurons to respond to different stimuli conditions (deprivation or imposition) by changing their optimal properties acquired after birth. This adaptation of neurons for visual perception suggests the existence

Adaptation-induced-plasticity of orientation in primary visual cortex is characterized by authors as the ability of cortical neurons to change their preferred orientation following a long [11, 61, 63] or short [62, 65] exposure to a non-preferred orientation for the primary

Attractive shifts are more frequent than repulsive shifts in longer adaptation durations (≥ 6 min) [11, 61]. Repeated or prolonged exposure to an adapter diminished neuronal responses evoked by the original optimal properties, furthermore in parallel, if it is the neuron's preferred stimulus [61]. Optical imaging investigations in recent years have also revealed the impact of adaptation-induced-plasticity, showing that orientation maps in V1 can be modified by imposing one particular orientation [62, 65, 67].

Adaptation and Neuronal Network in Visual Cortex 331

crosscorrelograms when one neuron is reference and other target. A shifted crosscorrelogram is a histogram obtained, when the spikes of the reference cell are shifted by one or two cycles of stimulation. This eliminates the possibility of stimulus-induced-relationship between two neurons. After this the shifted crosscorrelogram is subtracted (corrected) to remove the stimulus-locked -component. Figure 5a corresponds to target cell projecting onto the reference cell. Target neuron fires few milliseconds before the reference cell since the peak of the crosscorrelogram appears few milliseconds before zero, that is, offset from zero, within 5ms. This means there is an excitation from target to the reference cell [79, 81]. Figure 5b corresponds to the reference cell projecting onto the target cell. Target neuron fires few milliseconds after the reference cell since the peak of the crosscorrelogram appears few milliseconds after zero, within 5ms. This means the excitation is from reference cell to the target cell [79, 81]. Figure 5c corresponds to the synchrony between two neurons, as the peak straddles zero [73, 82]. This means there is a common excitatory input to both neurons most likely from other neuron or neurons. Though, various time windows have been taken into consideration ranging from 3ms to 10 ms [72, 79] to reveal the functional connections between

the involved neurons, but a time window within 5ms is most frequently used.

**Figure 6.** Differential effects of adaptation on synchrony for responses evoked by original orientation

and adapting orientation

## **5. Crosscorrelograms and neuronal relationships in visual cortex**

As reviewed above [11, 49, 61-65], adaptation- induced studies on visual cortex can reveal a great deal about the functioning of the visual cortex. Crosscorrelogram analysis is an efficient tool to establish the functional connectivity between neurons. Ever since the crosscorrelogram approach was introduced [68], it has proved to be an invaluable tool to determine how specific neurons interact with each other. A crosscorrelogram is a histogram used to infer the connectivity between two neurons, where one neuron is reference and other target. The histogram shows us when the spikes of target neuron are related in time to the spikes of reference neuron. The technique has been instrumental in revealing widespread incidences of neuronal synchrony and neuronal time-relationships among various cortical areas [64, 70-78]. For instance, as revealed by crosscorrelogram analysis, synchrony has been reported to be strong between cells with similar preferred parameters due in part to specific connections between cortical domains having similar tuning properties. Thus, based on the crosscorrelogram analysis, the functional network connections can be established between the neurons [58, 71, 79-81].

**Figure 5.** Crosscorrelograms between two neurons (reference and target)

A typical crosscorrelogram between two neurons to interpret the relation between them is obtained by keeping one of the neurons as reference and calculating the spikes of the other neuron with reference to it. An investigator generally is interested in one of the following patterns as illustrated in figure 5, while he is interpreting crosscorrelograms. Figure 5 illustrates the time relations between two neurons as revealed by shifted and corrected crosscorrelograms when one neuron is reference and other target. A shifted crosscorrelogram is a histogram obtained, when the spikes of the reference cell are shifted by one or two cycles of stimulation. This eliminates the possibility of stimulus-induced-relationship between two neurons. After this the shifted crosscorrelogram is subtracted (corrected) to remove the stimulus-locked -component. Figure 5a corresponds to target cell projecting onto the reference cell. Target neuron fires few milliseconds before the reference cell since the peak of the crosscorrelogram appears few milliseconds before zero, that is, offset from zero, within 5ms. This means there is an excitation from target to the reference cell [79, 81]. Figure 5b corresponds to the reference cell projecting onto the target cell. Target neuron fires few milliseconds after the reference cell since the peak of the crosscorrelogram appears few milliseconds after zero, within 5ms. This means the excitation is from reference cell to the target cell [79, 81]. Figure 5c corresponds to the synchrony between two neurons, as the peak straddles zero [73, 82]. This means there is a common excitatory input to both neurons most likely from other neuron or neurons. Though, various time windows have been taken into consideration ranging from 3ms to 10 ms [72, 79] to reveal the functional connections between the involved neurons, but a time window within 5ms is most frequently used.

330 Visual Cortex – Current Status and Perspectives

modified by imposing one particular orientation [62, 65, 67].

connections can be established between the neurons [58, 71, 79-81].

**Figure 5.** Crosscorrelograms between two neurons (reference and target)

A typical crosscorrelogram between two neurons to interpret the relation between them is obtained by keeping one of the neurons as reference and calculating the spikes of the other neuron with reference to it. An investigator generally is interested in one of the following patterns as illustrated in figure 5, while he is interpreting crosscorrelograms. Figure 5 illustrates the time relations between two neurons as revealed by shifted and corrected

Attractive shifts are more frequent than repulsive shifts in longer adaptation durations (≥ 6 min) [11, 61]. Repeated or prolonged exposure to an adapter diminished neuronal responses evoked by the original optimal properties, furthermore in parallel, if it is the neuron's preferred stimulus [61]. Optical imaging investigations in recent years have also revealed the impact of adaptation-induced-plasticity, showing that orientation maps in V1 can be

As reviewed above [11, 49, 61-65], adaptation- induced studies on visual cortex can reveal a great deal about the functioning of the visual cortex. Crosscorrelogram analysis is an efficient tool to establish the functional connectivity between neurons. Ever since the crosscorrelogram approach was introduced [68], it has proved to be an invaluable tool to determine how specific neurons interact with each other. A crosscorrelogram is a histogram used to infer the connectivity between two neurons, where one neuron is reference and other target. The histogram shows us when the spikes of target neuron are related in time to the spikes of reference neuron. The technique has been instrumental in revealing widespread incidences of neuronal synchrony and neuronal time-relationships among various cortical areas [64, 70-78]. For instance, as revealed by crosscorrelogram analysis, synchrony has been reported to be strong between cells with similar preferred parameters due in part to specific connections between cortical domains having similar tuning properties. Thus, based on the crosscorrelogram analysis, the functional network

**5. Crosscorrelograms and neuronal relationships in visual cortex** 

**Figure 6.** Differential effects of adaptation on synchrony for responses evoked by original orientation and adapting orientation

Figure 6 corresponds to differential effects of adaptation on synchrony for responses evoked by original orientation and adapting orientation. Figure 6a represents respective spikewaveforms for cell 1 and cell 2. Figure 6b shows crosscorrelograms between cell 1 and cell 2 before adaptation (control). The centered peak corresponds to synchrony. Fig 6c represents crosscorrelograms between cell 1 and cell 2 after adaptation. Synchrony disappeared for original optimal orientation while it persists for responses evoked by adapter. Figure 6d shows respective Peri-Stimulus Time Histograms (PSTH's) for cells in control (before adaptation). Figure 6e corresponds to respective Peri-Stimulus Time Histograms (PSTH's) for cells after adaptation. The downward black arrows indicate onset of the drifting sinewave patch positioned in the receptive field.

Adaptation and Neuronal Network in Visual Cortex 333

**Figure 7.** Functional relationships between simultaneously recorded cells before and after adaptation

between all the involved neurons were obtained. A time window of 5 ms before or after the zero in the shifted and corrected crosscorrelogram is taken into account for the projection to be valid. The physiological connectivity (synaptic connections, common input) between cells occurs on very small time scales, less than 3 ms [79, 81]. Since we consider a time window of 10 ms in crosscorrelograms for establishing connections, therefore, this connectivity only reflects that the cells function (irrespective of physical connectivity) in coordination with each other in a time-window of 10 ms following a presented stimulus. Figure 7a illustrates the functional connections between three neurons as revealed by their respective crosscorrelograms (shifted and corrected). White projections correspond to established connections before adaptation. Figure 7b illustrates the new functional connections between same neurons as revealed by their respective crosscorrelograms (shifted and corrected). Yellow projection corresponds to the new connection established after adaptation. Dotted

Since adaptation modifies the optimal properties of neurons, whether orientation, direction or spatial frequency, it seems reasonable to postulate that these modifications following adaptation induce a rapid reorganization of the inter-neuronal relationships, as revealed by crosscorrelogram analyses. For instance, a recipient neuron programmed since birth to be connected to a specific neuron that responds optimally to one specific property, all of a sudden starts responding optimally to another stimulus, and begins participating in a different network with other neuron- as if breaking its allegiance to the neuron it is programmed to be connected since birth.

## **6. Network formation**

Neurons do not respond in isolation to the trigger features, but in coordination with surrounding neurons. Thus, they encode stimuli features by forming cell assemblies, where in the involved neurons are time related with each other. Recent investigations have revealed the ability of visual neurons to respond to different stimuli conditions by changing their optimal properties acquired after birth. Most of these studies have been done by visual deprivation [9, 83-86], whereas only a few have centered on induced adaptation [11, 62, 64, 87]. This adaptation of neurons to non-optimal stimuli suggests the adaptability of neuronal code to visual stimuli.

Neuronal connections in the cortex generally occur locally [79, 88]. Visual cortex is a highly specialized functional area where the neurons coordinate locally to encode the visual scenes [89, 90, 91]. To reveal how this local circuitry of different neurons in visual cortex is set up and modulated in response to different visual stimuli is of prime importance to understand the mechanisms of information processing. Crosscorrelogram strategy discussed above can be effectively applied to form a neuronal network in response to visual stimuli. Thus, it can be an efficient tool in deciphering the changes in the neuronal code post-adaptation, hence, the mechanisms of plastic modifications can be revealed.

For example, in the Figure 7 we show the network of connections prior and post adaptation between three neurons recorded simultaneously from the same electrode lowered into the primary visual cortex of an anaesthetized cat. In this experiment, a stimulating sine-wave drifting grating was set to excite cells optimally. Shifted and corrected crosscorrlegrams

wave patch positioned in the receptive field.

programmed to be connected since birth.

the mechanisms of plastic modifications can be revealed.

**6. Network formation** 

code to visual stimuli.

Figure 6 corresponds to differential effects of adaptation on synchrony for responses evoked by original orientation and adapting orientation. Figure 6a represents respective spikewaveforms for cell 1 and cell 2. Figure 6b shows crosscorrelograms between cell 1 and cell 2 before adaptation (control). The centered peak corresponds to synchrony. Fig 6c represents crosscorrelograms between cell 1 and cell 2 after adaptation. Synchrony disappeared for original optimal orientation while it persists for responses evoked by adapter. Figure 6d shows respective Peri-Stimulus Time Histograms (PSTH's) for cells in control (before adaptation). Figure 6e corresponds to respective Peri-Stimulus Time Histograms (PSTH's) for cells after adaptation. The downward black arrows indicate onset of the drifting sine-

Since adaptation modifies the optimal properties of neurons, whether orientation, direction or spatial frequency, it seems reasonable to postulate that these modifications following adaptation induce a rapid reorganization of the inter-neuronal relationships, as revealed by crosscorrelogram analyses. For instance, a recipient neuron programmed since birth to be connected to a specific neuron that responds optimally to one specific property, all of a sudden starts responding optimally to another stimulus, and begins participating in a different network with other neuron- as if breaking its allegiance to the neuron it is

Neurons do not respond in isolation to the trigger features, but in coordination with surrounding neurons. Thus, they encode stimuli features by forming cell assemblies, where in the involved neurons are time related with each other. Recent investigations have revealed the ability of visual neurons to respond to different stimuli conditions by changing their optimal properties acquired after birth. Most of these studies have been done by visual deprivation [9, 83-86], whereas only a few have centered on induced adaptation [11, 62, 64, 87]. This adaptation of neurons to non-optimal stimuli suggests the adaptability of neuronal

Neuronal connections in the cortex generally occur locally [79, 88]. Visual cortex is a highly specialized functional area where the neurons coordinate locally to encode the visual scenes [89, 90, 91]. To reveal how this local circuitry of different neurons in visual cortex is set up and modulated in response to different visual stimuli is of prime importance to understand the mechanisms of information processing. Crosscorrelogram strategy discussed above can be effectively applied to form a neuronal network in response to visual stimuli. Thus, it can be an efficient tool in deciphering the changes in the neuronal code post-adaptation, hence,

For example, in the Figure 7 we show the network of connections prior and post adaptation between three neurons recorded simultaneously from the same electrode lowered into the primary visual cortex of an anaesthetized cat. In this experiment, a stimulating sine-wave drifting grating was set to excite cells optimally. Shifted and corrected crosscorrlegrams

**Figure 7.** Functional relationships between simultaneously recorded cells before and after adaptation

between all the involved neurons were obtained. A time window of 5 ms before or after the zero in the shifted and corrected crosscorrelogram is taken into account for the projection to be valid. The physiological connectivity (synaptic connections, common input) between cells occurs on very small time scales, less than 3 ms [79, 81]. Since we consider a time window of 10 ms in crosscorrelograms for establishing connections, therefore, this connectivity only reflects that the cells function (irrespective of physical connectivity) in coordination with each other in a time-window of 10 ms following a presented stimulus. Figure 7a illustrates the functional connections between three neurons as revealed by their respective crosscorrelograms (shifted and corrected). White projections correspond to established connections before adaptation. Figure 7b illustrates the new functional connections between same neurons as revealed by their respective crosscorrelograms (shifted and corrected). Yellow projection corresponds to the new connection established after adaptation. Dotted

gray projection represents the disappeared projection. PC corresponds to the probability coefficient of the connections. Solid green pyramid represents a pyramidal cell and the solid red sphere represents an interneuron. Red curve line indicates 95% significance level.

Adaptation and Neuronal Network in Visual Cortex 335

adaptation the inter-neuronal relationships are modified. This suggests that the entire cortical network reorganises itself post adaptation, that is, a new cortex is formed, as if

To sum it up, it is of prime importance to understand the plastic modifications of brain for various fundamental and medical reasons. This chapter underlined the importance of imposed adaptation studies within brain, particularly in primary visual cortex based on the crosscorrelogram analysis, framing a premise to better understand the functional connectivity [79, 92] and mechanisms in local neuronal circuits between various identified neurons, at least between the pyramidal cells and interneurons before and after adaptation. and post adaptation, thus, could help us to decipher the mechanisms of information

[1] Hubel DH, Wiesel TN (1959) Receptive Fields of Single Neurones in the Cat's Striate

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[8] Hubel DH, Wiesel TN (1963b) Receptive Fields of Cells in Striate Cortex of Very Young,

designed for changed properties.

**Author details** 

Stéphane Molotchnikoff\*\*†

Cortex. J Physiol. 148: 574-591.

Ophthalmol. 11: 346-354.

Both Authors Contributed Equally

\*\*†Corresponding Author

Cortex. J Physiol. 165: 559-568.

Striate Cortex. J Physiol. 195: 215-243.

Lyes Bachatene\*

**8. References** 

249: 445-468.

8: 372-379.

 \*

processing, hence the neuronal codes governing them.

and Vishal Bharmauria\*

*Department of Biological Sciences, University of Montreal, Montreal, Canada* 

*Department of Biological Sciences, University of Montreal, Montreal, Canada* 

Architecture in the Cat's Visual Cortex. J Physiol.160: 106-154.

Visually Inexperienced Kittens. J Neurophysiol. 26: 994-1002.

This changing of connections indicates that the functional relationships between neurons are modified depending on the stimulus features. For instance, following the forceful presentation of a particular stimulus (in this example a different orientation) results in disappearance of some relationships, and appearance of new relationships.

Based on the above example, we hypothesize and propose a model how the network of neurons is modulated prior and post adaptation as revealed by the functional timerelationship of neurons between them. Figure 8 depicts the functional connections between the same neurons before and after adaptation. White projections in figure 8a show the projections that cells have onto each other before adaptation, whereas figure 8b depicts how the network changes in the same group of neurons after adaptation. Some of the connections between the cells remain distinct (white projections) whereas some connections disappear (dotted gray arrows) with appearing new connections (yellow projections).

**Figure 8.** Network model before and after adaptation

## **7. Conclusion**

This chapter reviewed the changes in the cellular properties post-adaptation. Indeed, the optimal trigger features may change following the prolonged application of a stimulus to which the cell responded feebly before adaptation. This phenomenon has been virtually observed in all mammals which have been tested so far. Also in parallel, following adaptation the inter-neuronal relationships are modified. This suggests that the entire cortical network reorganises itself post adaptation, that is, a new cortex is formed, as if designed for changed properties.

To sum it up, it is of prime importance to understand the plastic modifications of brain for various fundamental and medical reasons. This chapter underlined the importance of imposed adaptation studies within brain, particularly in primary visual cortex based on the crosscorrelogram analysis, framing a premise to better understand the functional connectivity [79, 92] and mechanisms in local neuronal circuits between various identified neurons, at least between the pyramidal cells and interneurons before and after adaptation. and post adaptation, thus, could help us to decipher the mechanisms of information processing, hence the neuronal codes governing them.

## **Author details**

334 Visual Cortex – Current Status and Perspectives

gray projection represents the disappeared projection. PC corresponds to the probability coefficient of the connections. Solid green pyramid represents a pyramidal cell and the solid

This changing of connections indicates that the functional relationships between neurons are modified depending on the stimulus features. For instance, following the forceful presentation of a particular stimulus (in this example a different orientation) results in

Based on the above example, we hypothesize and propose a model how the network of neurons is modulated prior and post adaptation as revealed by the functional timerelationship of neurons between them. Figure 8 depicts the functional connections between the same neurons before and after adaptation. White projections in figure 8a show the projections that cells have onto each other before adaptation, whereas figure 8b depicts how the network changes in the same group of neurons after adaptation. Some of the connections between the cells remain distinct (white projections) whereas some connections disappear

This chapter reviewed the changes in the cellular properties post-adaptation. Indeed, the optimal trigger features may change following the prolonged application of a stimulus to which the cell responded feebly before adaptation. This phenomenon has been virtually observed in all mammals which have been tested so far. Also in parallel, following

red sphere represents an interneuron. Red curve line indicates 95% significance level.

disappearance of some relationships, and appearance of new relationships.

(dotted gray arrows) with appearing new connections (yellow projections).

**Figure 8.** Network model before and after adaptation

**7. Conclusion** 

Lyes Bachatene\* and Vishal Bharmauria\* *Department of Biological Sciences, University of Montreal, Montreal, Canada* 

Stéphane Molotchnikoff\*\*† *Department of Biological Sciences, University of Montreal, Montreal, Canada* 

## **8. References**


<sup>\*</sup> Both Authors Contributed Equally

<sup>\*\*†</sup>Corresponding Author

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**Chapter 16** 

© 2012 Tanaka, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

distribution, and reproduction in any medium, provided the original work is properly cited.

On the other hand, numerous studies have been devoted to solving a question of how orientation selectivity is established and elaborated in early life. It is believed that orientation selectivity innately emerges prior to visual experience (Albus & Wolf, 1984;

and reproduction in any medium, provided the original work is properly cited.

**New Pictures of the Structure and Plasticity** 

**of Orientation Columns in the Visual Cortex** 

Orientation selectivity of neurons in the primary visual cortex is thought to be an important requisite for the preprocessing of visual information, which is followed by more complex information processing and representation in the extrastriate cortex for visual perception. It is widely accepted that neurons in the primary visual cortex optimally responding to similar stimulus orientations are clustered in a manner of straight columns extending from the superficial to deep layers (Hubel & Wiesel, 1962, 1963a). The cerebral cortex is, however, folded inside a skull, which makes gyri and fundi. Particularly, in cats, area 17 (primary visual cortex) is located on the curved cortex called the lateral gyrus (Tusa et al., 1978). These facts raise questions of how the tangential arrangement of orientation columns is reconciled with the curvature of the gyrus, and whether the columns penetrate the cortex from the superficial to deep layers. In the first part of this chapter, we show a possible configuration of feature representation in the visual cortex using a three-dimensional (3D) self-organization model, and then confirm the predicted 3D orientation representation using multi-slice, high-resolution functional magnetic resonance imaging (fMRI) performed in the cat visual cortex (Tanaka et al., 2011). We obtained a close agreement in orientation representation between theoretical predictions and experimental observations. These studies demonstrated that in the curved cortex, preferred orientations are represented by wedgelike orientation columns which do not necessarily penetrate from superficial to deep layers, whereas in the flat cortex, preferred orientations are tended to be represented by classical

Shigeru Tanaka

**1. Introduction** 

straight columns.

http://dx.doi.org/10.5772/48396

Additional information is available at the end of the chapter


## **New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex**

Shigeru Tanaka

340 Visual Cortex – Current Status and Perspectives

Populations.Nature. 452: 220-4.

Neurophysiol. 89: 943-953.

5: 18.

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Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48396

## **1. Introduction**

Orientation selectivity of neurons in the primary visual cortex is thought to be an important requisite for the preprocessing of visual information, which is followed by more complex information processing and representation in the extrastriate cortex for visual perception. It is widely accepted that neurons in the primary visual cortex optimally responding to similar stimulus orientations are clustered in a manner of straight columns extending from the superficial to deep layers (Hubel & Wiesel, 1962, 1963a). The cerebral cortex is, however, folded inside a skull, which makes gyri and fundi. Particularly, in cats, area 17 (primary visual cortex) is located on the curved cortex called the lateral gyrus (Tusa et al., 1978). These facts raise questions of how the tangential arrangement of orientation columns is reconciled with the curvature of the gyrus, and whether the columns penetrate the cortex from the superficial to deep layers. In the first part of this chapter, we show a possible configuration of feature representation in the visual cortex using a three-dimensional (3D) self-organization model, and then confirm the predicted 3D orientation representation using multi-slice, high-resolution functional magnetic resonance imaging (fMRI) performed in the cat visual cortex (Tanaka et al., 2011). We obtained a close agreement in orientation representation between theoretical predictions and experimental observations. These studies demonstrated that in the curved cortex, preferred orientations are represented by wedgelike orientation columns which do not necessarily penetrate from superficial to deep layers, whereas in the flat cortex, preferred orientations are tended to be represented by classical straight columns.

On the other hand, numerous studies have been devoted to solving a question of how orientation selectivity is established and elaborated in early life. It is believed that orientation selectivity innately emerges prior to visual experience (Albus & Wolf, 1984;

© 2012 Tanaka, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Blakemore & Van Sluyters, 1975; Crair et al., 1998; Hubel & Wiesel, 1963b), but there has been a debate on the issue whether visual experience can modify orientation selectivity. Historically, Blakemore & Cooper (1970) reported that orientation-restricted visual experience modified response properties of visual cortical neurons so as to respond selectively to experienced orientations, which suggests a validity of the instruction hypothesis: Preferred orientations of neurons shift to experienced orientations (Rauschecker & Singer, 1981). Later, Stryker et al. (1978) and Carlson et al. (1986) claimed an objection against this hypothesis and proposed the suppression hypothesis: Neurons innately selective for inexperienced orientations only diminish their responsiveness while preferred orientations do not change. The current consensus of orientation plasticity in early life favors the suppression hypothesis. However, considering that visual experience for a few days under monocular deprivation shifts ocular dominance of visual cortical neurons towards an open eye (Wiesel & Hubel, 1963), one may not feel that the selection hypothesis is convincing. In the second part of this chapter, we show data obtained from *in vivo* intrinsic signal optical imaging in the visual cortex of kittens reared with head-mounted cylindrical-lens-fitted goggles for stable exposure to a single orientation (Tanaka et al., 2004, 2006, 2007). It was revealed that single-orientation exposure expands the cortical territory that represents the exposed orientation, as a previous study by Sengpiel et al. (1999). However, the degree of overrepresentation of the exposed orientation was more prominent in our data due to several differences of visual experience manipulation. Also, we show an age-dependent sensitive period profile for orientation selectivity in the visual cortex of goggle-reared kittens (Tanaka et al. 2009).

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 343

We extended the previously reported model (Nakagama & Tanaka, 2004) to describe the formation of orientation, direction, ocular dominance and retinotopic representation in the 3D primary visual cortex. For simplicity, among 6 layers of the visual cortex, we omitted layer 1, in which massive fibers are running tangentially and neurons exist sparsely. In the other 5 layers, we assumed that each neuron represents *a priori* a set of visual features such as ocular dominance, orientation preference, direction-of-motion preference and the center of the receptive field for simplicity. The cortical arrangement of the visual features was modified by local intracortical interaction among neurons, which change an initial random arrangement of

One of the purposes of this study is to investigate the effect of the global curvature of the visual cortex on visual feature representation. For this purpose, we assumed a ''stadiumshape'' of the model cortex with a finite thickness, which is embedded inside a box composed of 128 × 128 × 72 voxels. Each voxel represents a cortical neuron. The outer and inner surfaces, respectively, correspond to the cortical surface and the boundary between the gray and white matter. The model cortical depth was set at 30 voxels with a 6-voxel thickness of each layer from layer 2 to layer 6. We assumed the thickness of the model cortex to be 2 mm, meaning one voxel is 66 μm. The periodic boundary condition was adopted to minimize a small-size effect. The feature space (FS) is given by the direct product of the space of the visual field (VF), the circular symmetric space of the preferred direction (PD) and the space of ocular dominance (OD): FS = VF×PD×OD. Space VF is the 2D visual field (26×54 grids), in which the position of a receptive field center of a cortical neuron is defined. The periodic boundary condition was imposed on space VF so as to be consistent with the periodic boundary condition imposed on the model visual cortex. The preferred orientation is defined as the axis orthogonal to the preferred direction of motion. The space OD is subdivided into five groups, in which groups 1 and 5 are driven exclusively by either of the two eyes, group 3 is driven by the two eyes equally, and groups 2 and 4 receive imbalanced

We assume that a neuron located at *j* in the model visual cortex represents a set of visual features (*k*, *θ*, *μ*), where *k*, *θ* and *μ* indicate the position of the receptive field, preferred direction and ocular dominance, respectively. The ocular dominance *μ* takes a value out of −1, −1/2, 0, 1/2, and 1 : *μ* = 1 represents exclusively the left-eye dominance and *μ* = −1 exclusively the right-eye dominance. The preferred direction *θ* takes one of 16 values out of 0, *π*/8, 2*π*/8, . . . , and 15*π*/8. The preferred orientation was assumed to be orthogonal to the preferred direction and invariant under rotational transformation by *π*. When a cortical

takes 1, and otherwise, 0. Here we assume that any

 

visual features to form an orderly arrangement over the 3D primary visual cortex.

*2.1.1. Three-dimensional model visual cortex and visual feature space* 

**2.1. Mathematical modeling** 

inputs from the two eyes.

*2.1.2. Energy function* 

neuron *j* represents (*k*, *θ*, *μ*), *j k*, , ,

## **2. Theory and experiment on 3D orientation representation**

We employed a self-organization model with minimal assumptions to describe the formation of joint maps of the preferred orientation, preferred direction, ocular dominance and retinotopy. We performed computer simulation using a structural annealing: Simulation was started only in the middle layer at the beginning and then the simulation range was expanded gradually to other layers. This annealing method well reproduced orientation columns vertically spanning all the layers in the flat cortex, consistent with widely accepted columnar organization. On the other hand, in the curved parts, orientation straight columns were disrupted and preferred orientations were clustered in wedge-like forms. It was likely that preferred directions are reversed in the deeper layers. Singularities associated with orientation representation appeared as warped lines in the 3D model cortex. Direction reversal appeared on the sheets that were delimited by orientation-singularity lines. These structures emerged from the balance between periodic arrangements of preferred orientations and vertical alignment of the same orientations. Then, to examine the biological plausibility of the simulation results, we attempted to visualize orientation representation in the cat visual cortex using multi-slice, high-resolution fMRI. We obtained a close agreement in orientation representation between theoretical predictions and experimental observations (Tanaka et al., 2011).

### **2.1. Mathematical modeling**

342 Visual Cortex – Current Status and Perspectives

goggle-reared kittens (Tanaka et al. 2009).

experimental observations (Tanaka et al., 2011).

**2. Theory and experiment on 3D orientation representation** 

We employed a self-organization model with minimal assumptions to describe the formation of joint maps of the preferred orientation, preferred direction, ocular dominance and retinotopy. We performed computer simulation using a structural annealing: Simulation was started only in the middle layer at the beginning and then the simulation range was expanded gradually to other layers. This annealing method well reproduced orientation columns vertically spanning all the layers in the flat cortex, consistent with widely accepted columnar organization. On the other hand, in the curved parts, orientation straight columns were disrupted and preferred orientations were clustered in wedge-like forms. It was likely that preferred directions are reversed in the deeper layers. Singularities associated with orientation representation appeared as warped lines in the 3D model cortex. Direction reversal appeared on the sheets that were delimited by orientation-singularity lines. These structures emerged from the balance between periodic arrangements of preferred orientations and vertical alignment of the same orientations. Then, to examine the biological plausibility of the simulation results, we attempted to visualize orientation representation in the cat visual cortex using multi-slice, high-resolution fMRI. We obtained a close agreement in orientation representation between theoretical predictions and

Blakemore & Van Sluyters, 1975; Crair et al., 1998; Hubel & Wiesel, 1963b), but there has been a debate on the issue whether visual experience can modify orientation selectivity. Historically, Blakemore & Cooper (1970) reported that orientation-restricted visual experience modified response properties of visual cortical neurons so as to respond selectively to experienced orientations, which suggests a validity of the instruction hypothesis: Preferred orientations of neurons shift to experienced orientations (Rauschecker & Singer, 1981). Later, Stryker et al. (1978) and Carlson et al. (1986) claimed an objection against this hypothesis and proposed the suppression hypothesis: Neurons innately selective for inexperienced orientations only diminish their responsiveness while preferred orientations do not change. The current consensus of orientation plasticity in early life favors the suppression hypothesis. However, considering that visual experience for a few days under monocular deprivation shifts ocular dominance of visual cortical neurons towards an open eye (Wiesel & Hubel, 1963), one may not feel that the selection hypothesis is convincing. In the second part of this chapter, we show data obtained from *in vivo* intrinsic signal optical imaging in the visual cortex of kittens reared with head-mounted cylindrical-lens-fitted goggles for stable exposure to a single orientation (Tanaka et al., 2004, 2006, 2007). It was revealed that single-orientation exposure expands the cortical territory that represents the exposed orientation, as a previous study by Sengpiel et al. (1999). However, the degree of overrepresentation of the exposed orientation was more prominent in our data due to several differences of visual experience manipulation. Also, we show an age-dependent sensitive period profile for orientation selectivity in the visual cortex of

We extended the previously reported model (Nakagama & Tanaka, 2004) to describe the formation of orientation, direction, ocular dominance and retinotopic representation in the 3D primary visual cortex. For simplicity, among 6 layers of the visual cortex, we omitted layer 1, in which massive fibers are running tangentially and neurons exist sparsely. In the other 5 layers, we assumed that each neuron represents *a priori* a set of visual features such as ocular dominance, orientation preference, direction-of-motion preference and the center of the receptive field for simplicity. The cortical arrangement of the visual features was modified by local intracortical interaction among neurons, which change an initial random arrangement of visual features to form an orderly arrangement over the 3D primary visual cortex.

#### *2.1.1. Three-dimensional model visual cortex and visual feature space*

One of the purposes of this study is to investigate the effect of the global curvature of the visual cortex on visual feature representation. For this purpose, we assumed a ''stadiumshape'' of the model cortex with a finite thickness, which is embedded inside a box composed of 128 × 128 × 72 voxels. Each voxel represents a cortical neuron. The outer and inner surfaces, respectively, correspond to the cortical surface and the boundary between the gray and white matter. The model cortical depth was set at 30 voxels with a 6-voxel thickness of each layer from layer 2 to layer 6. We assumed the thickness of the model cortex to be 2 mm, meaning one voxel is 66 μm. The periodic boundary condition was adopted to minimize a small-size effect. The feature space (FS) is given by the direct product of the space of the visual field (VF), the circular symmetric space of the preferred direction (PD) and the space of ocular dominance (OD): FS = VF×PD×OD. Space VF is the 2D visual field (26×54 grids), in which the position of a receptive field center of a cortical neuron is defined. The periodic boundary condition was imposed on space VF so as to be consistent with the periodic boundary condition imposed on the model visual cortex. The preferred orientation is defined as the axis orthogonal to the preferred direction of motion. The space OD is subdivided into five groups, in which groups 1 and 5 are driven exclusively by either of the two eyes, group 3 is driven by the two eyes equally, and groups 2 and 4 receive imbalanced inputs from the two eyes.

#### *2.1.2. Energy function*

We assume that a neuron located at *j* in the model visual cortex represents a set of visual features (*k*, *θ*, *μ*), where *k*, *θ* and *μ* indicate the position of the receptive field, preferred direction and ocular dominance, respectively. The ocular dominance *μ* takes a value out of −1, −1/2, 0, 1/2, and 1 : *μ* = 1 represents exclusively the left-eye dominance and *μ* = −1 exclusively the right-eye dominance. The preferred direction *θ* takes one of 16 values out of 0, *π*/8, 2*π*/8, . . . , and 15*π*/8. The preferred orientation was assumed to be orthogonal to the preferred direction and invariant under rotational transformation by *π*. When a cortical neuron *j* represents (*k*, *θ*, *μ*), *j k*, , , takes 1, and otherwise, 0. Here we assume that any

cortical neuron is supposed to represent only one set of visual features. The state of the visual feature arrangements is characterized by the energy function:

$$H = -\sum\_{\langle j,\uparrow\rangle} \sum\_{k,\theta,\mu k',\theta',\mu'} V\_{\langle j,\uparrow\rangle} \Gamma\_{k,\theta,\mu k',\theta',\mu} \sigma\_{\langle j,k,\theta,\mu\rangle} \sigma\_{\langle j,k',\theta',\mu'\rangle} + c \sum\_{l=2}^{6} \sum\_{k,\theta,\mu} \left( \sum\_{j\neq l\text{-th layer}} \sigma\_{\langle j,k,\theta,\mu\rangle} \right)^2,\tag{1}$$

where *Vj j*, represents the 3D cortical interaction between a pair of visual features represented by neurons *j* and *j′*. This interaction is determined by the convolution of the dendritic arbor function with the axonal arbor function. It is know that in the adult cortex, there are longrange tangential and inter-layer connections. However, we assume that the axonal arbor of nearby cortical neurons mainly works for column formation in the early developmental stage, and we omit the long-range connections. This assumption indicates that the interaction does not depend on the global morphology of the cortex and is identical for any positions either in the superficial layers or deep layers. Therefore, we identify the cortical interaction as the dendritic integration, which is simply given by the Gaussian function:

$$V\_{j,f} = \frac{1}{2\pi\lambda\_v^2} \exp\left(-\frac{d\_{j,f}^2}{2\lambda\_v^2}\right) \tag{2}$$

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 345

(5)

(6)

, which is set at 1.0. The

(7)

1 or 1 ),

 

2 2

(1 ) 1 ' . <sup>2</sup> *r r*

directional component of the correlation function is given as a Fourier cosine series. The order of *m*=1 represents the correlation between a pair of preferred directions, whereas the order of *m*=2 represents the correlation between a pair of preferred axes of motion, which

At the beginning of the simulation, we randomize visual features in the model cortex. In the

because the layer is geniculocortical input layer. In the other layers, neurons receive random inputs with ocular dominance of either of −1, −1/2, 0, 1/2, or 1, and preferred direction of either of 0, / 8, 2 / 8, ···, or 15 / 8. The RF center position is also randomized retaining

; 2

where ; *kJ j <sup>d</sup>* represents the distance between cortical position *j* and position *Jk* that retinotopically corresponds to the position *k* in the visual field, where *λ*Ret represents the roughness of retinotopy (*λ*Ret = 3.5). For each trial of updating visual features in the simulation, a new candidate for a set of visual features is selected according to the same probability distribution as in the initial randomization, and then the old set of features is replaced with the new one according to the probability determined by the energy difference

<sup>1</sup> Pr( ) . 1 exp[ ( )] *after before before after*

Here, *Hbefore* and *Hafter* represent the energy before and after the trial of replacement, respectively. The parameter *β*, which determines the steepness of the logistic function of the right-hand side of Eq. (8) in the transition region, was set at *β* = . Thereby, the probability takes 1 when *Hafter* is smaller than *Hbefore*, otherwise 0. The repetition of the update procedure likely decreases the energy of the system and realizes an equilibrium cortical arrangement of

In the present model, in which the cortical interaction function is isotropic, clustering of similar visual features occurs but columnar organization cannot be formed even in flat parts

*H H*

(8)

2 ;

 

*d*

Ret exp , <sup>2</sup> *kJ j*

1 2 cos , *<sup>m</sup>*

*m*

*a am*

, 0

,

middle layer of the cortex, neurons receive random inputs from either eye (

*k j*

*A*

visual features when the change in energy becomes negligibly small.

between the states before and after the trial of replacement:

 

is the positional correlation length equal to 2 *ref*

are orthogonal to the preferred orientations.

rough retinotopy according to the probability:

*2.1.3. Computer simulation* 

 

Here, *corr* 

where *<sup>v</sup>* is the excitatory interaction length in the cortex ( *<sup>v</sup>* =2.0 corresponding to 132 μm), and *j j*, ' *d* indicates the distance between cortical neurons *j* and *j′*. *k k* , ,;, , represents the correlation function of input signals. The second term of Eq. (1) represents a constraint on the representation of the visual features to avoid imbalanced representation and results in each layer covering all the feature space as uniformly as possible. The constraint is imposed layer by layer. The sum of *j* is taken over all neurons located within the *l*-th layer of the model cortex. The strength of constraint *c* was set at 0.008.

The correlation function of input signals *k k* ,,;, ' ', ' is modeled by assuming that local components of visual images obey white noise regarding the position and direction of motion with a strength of correlation between the two eyes *r,* and that the input signal receptive field is given by the product of the positional Gaussian function and the directional cosine series. After short calculations, the correlation functiont is rewritten as

$$
\Gamma\_{k,\theta,\mu;k',\theta',\mu'} = \mathcal{S}\_{k,k'} \Psi\_{\theta,\theta'} \mathcal{O}\_{\mu,\mu'} \tag{3}
$$

where *k k*, *S* , , and , represent component correlation functions regarding the position of receptive field centers in the visual field, direction of motion and ocular dominance, respectively. These component correlation functions are given by

$$S\_{k,k'} = \exp\left(-\frac{d\_{k,k'}^2}{2\mathcal{J}\_{corr}^2}\right) \tag{4}$$

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 345

$$\Psi\_{\theta,\theta'} = 2a\_0^2 + \sum\_{m=1}^{\oplus} a\_m^2 \cos m \left(\theta - \theta'\right),\tag{5}$$

$$\text{CO}\_{\mu,\mu'} = \frac{(1+r) + \left(1-r\right)\mu\mu'}{2}.\tag{6}$$

Here, *corr* is the positional correlation length equal to 2 *ref* , which is set at 1.0. The directional component of the correlation function is given as a Fourier cosine series. The order of *m*=1 represents the correlation between a pair of preferred directions, whereas the order of *m*=2 represents the correlation between a pair of preferred axes of motion, which are orthogonal to the preferred orientations.

#### *2.1.3. Computer simulation*

344 Visual Cortex – Current Status and Perspectives

 

where

where *k k*, *S* ,

 ,and

cortical neuron is supposed to represent only one set of visual features. The state of the

 

where *Vj j*, represents the 3D cortical interaction between a pair of visual features represented by neurons *j* and *j′*. This interaction is determined by the convolution of the dendritic arbor function with the axonal arbor function. It is know that in the adult cortex, there are longrange tangential and inter-layer connections. However, we assume that the axonal arbor of nearby cortical neurons mainly works for column formation in the early developmental stage, and we omit the long-range connections. This assumption indicates that the interaction does not depend on the global morphology of the cortex and is identical for any positions either in the superficial layers or deep layers. Therefore, we identify the cortical interaction as the

> , 2 2 1

μm), and *j j*, ' *d* indicates the distance between cortical neurons *j* and *j′*. *k k* ,

represents the correlation function of input signals. The second term of Eq. (1) represents a constraint on the representation of the visual features to avoid imbalanced representation and results in each layer covering all the feature space as uniformly as possible. The constraint is imposed layer by layer. The sum of *j* is taken over all neurons located within

> 

components of visual images obey white noise regarding the position and direction of motion with a strength of correlation between the two eyes *r,* and that the input signal receptive field is given by the product of the positional Gaussian function and the directional cosine series. After short calculations, the correlation functiont is rewritten as

, ,;, , , , ' ', ' , *k k kk S*

position of receptive field centers in the visual field, direction of motion and ocular

, <sup>2</sup> exp , <sup>2</sup>

 

 

2 ,

*d*

*k k*

*corr*

exp , 2 2

*v v*

, ,, , , 2 , , -th layer

 

*jjk k l k jl*

 

6

 

, ,, ; , , ,,, , , , ,,,

(1)

 

, *jj k k jk j k j k*

'

' ', ' is modeled by assuming that local

(3)

represent component correlation functions regarding the

2 ,

*d*

 

 

*j j*

2

 

(2)

*<sup>v</sup>* =2.0 corresponding to 132

 ,;, , 

(4)

visual feature arrangements is characterized by the energy function:

*H V c* 

dendritic integration, which is simply given by the Gaussian function:

'

the *l*-th layer of the model cortex. The strength of constraint *c* was set at 0.008.

 

dominance, respectively. These component correlation functions are given by

*k k*

*S*

The correlation function of input signals *k k* ,,;,

, *j j*

*<sup>v</sup>* is the excitatory interaction length in the cortex (

*V*

At the beginning of the simulation, we randomize visual features in the model cortex. In the middle layer of the cortex, neurons receive random inputs from either eye ( 1 or 1 ), because the layer is geniculocortical input layer. In the other layers, neurons receive random inputs with ocular dominance of either of −1, −1/2, 0, 1/2, or 1, and preferred direction of either of 0, / 8, 2 / 8, ···, or 15 / 8. The RF center position is also randomized retaining rough retinotopy according to the probability:

$$A\_{k;j} = \exp\left(-\frac{d\_{I\_k;j}^2}{2\mathcal{A}\_{\text{Ret}}^2}\right) \tag{7}$$

where ; *kJ j <sup>d</sup>* represents the distance between cortical position *j* and position *Jk* that retinotopically corresponds to the position *k* in the visual field, where *λ*Ret represents the roughness of retinotopy (*λ*Ret = 3.5). For each trial of updating visual features in the simulation, a new candidate for a set of visual features is selected according to the same probability distribution as in the initial randomization, and then the old set of features is replaced with the new one according to the probability determined by the energy difference between the states before and after the trial of replacement:

$$\Pr(before \rightarrow after) = \frac{1}{1 + \exp[\beta(H^{after} - H^{before})]}.\tag{8}$$

Here, *Hbefore* and *Hafter* represent the energy before and after the trial of replacement, respectively. The parameter *β*, which determines the steepness of the logistic function of the right-hand side of Eq. (8) in the transition region, was set at *β* = . Thereby, the probability takes 1 when *Hafter* is smaller than *Hbefore*, otherwise 0. The repetition of the update procedure likely decreases the energy of the system and realizes an equilibrium cortical arrangement of visual features when the change in energy becomes negligibly small.

In the present model, in which the cortical interaction function is isotropic, clustering of similar visual features occurs but columnar organization cannot be formed even in flat parts of the model visual cortex when the simulation is carried out to update features sampled randomly from the entire model cortex. So, we carried out the simulation according to the following schedule of spatial simulated annealing that we call hereafter, which assists the formation of iso-orientation domains continuously extending from layer 2 to layer 6. For simulation step *t* in the interval of 1 1 *t T* , simulation was performed only in the middle layer whose upper and lower boundaries were located at 13 and 18 voxels from the bottom of the model cortex. For 1 2 *T tT* , the inner boundary *inner <sup>z</sup>* and the outer boundary *outer <sup>z</sup>* of the simulation range in the depth direction was gradually changed as 1 2 1 13 12 *inner t T z T T* , and <sup>1</sup> 2 1 18 12 *outer t T z T T* . For 2 *T t* , simulation was carried

out in all model visual cortex. Here, one simulation step is defined as the trial numbers of updating features by the total number of voxels within which simulation is conducted. That is, for 1 simulation step, each voxel is tried to be updated, on average, once. Parameters 1 *T* and <sup>2</sup> *T* were set at 100 and 350 simulation steps. Thus, columnar structures of visual features are likely formed owing to the spatial simulated annealing, even if we do not assume anisotropic cortical interaction.

#### *2.1.4. Analyses of cortical feature representations*

An output response of neuron *j* to the presentation of visual images through either eye is given by the integration of evoked activities at nearby neurons through the dendrite. Therefore, the output neuronal response is expressed by

$$\boldsymbol{\eta}^{\rm out}\_{\boldsymbol{j};\boldsymbol{k},\boldsymbol{\theta},\boldsymbol{\mu}} = \sum\_{\boldsymbol{j}^{\prime},\boldsymbol{l},\boldsymbol{\phi}} \boldsymbol{V}\_{\boldsymbol{j};\boldsymbol{j}^{\prime}} \,\boldsymbol{\sigma}\_{\boldsymbol{j}^{\prime};\boldsymbol{l},\boldsymbol{\phi},\boldsymbol{\mu}} \,\boldsymbol{R}\_{\boldsymbol{l},\boldsymbol{\phi};\boldsymbol{k},\boldsymbol{\theta}}.\tag{9}$$

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 347

(12)

*j xj yj zj*

, at position

*<sup>j</sup>* are

(13)

,

;,,

 

*out j k*

 

To analyze orientation and direction singularities, we calculate an absolute value of the

gradient vector for the preferred orientation, <sup>222</sup> *pref pref pref pref*

.

*j xj yj zj* 

 

*x hy z x hy z*

2

*j j jj j jj*

*j jj j jj j*

,, ,,

,, ,, , <sup>2</sup>

 

*x y hz x y hz*

 

*xyz h xyz h*

,, ,, 2

*j jjj jjj*

where *h* indicates the distance between nearest neighbor model neurons, which is the size of the voxel corresponding 66μm. Plotting grey spots at positions where absolute values of orientation gradient vectors are larger than 0.01 rad/μm and those of direction gradient vectors are larger than 0.02 rad/μm, we obtain orientation-singularity lines and direction-

The positions of orientation pinwheel centers along the model cortical surface are determined automatically as the points around which the circular integration of the orientation difference amounts to just ±*π*, where the circular integration is taken in the counterclockwise direction. The counterclockwise or clockwise pinwheel centers are defined by the sign of the integral. Thus-defined pinwheel centers coincided with the position of high-gradient of preferred orientations along the cortical surface. The relative number of pinwheel centers within ocular dominance subregions is analyzed according to the previously introduced method (Hübener et al. 1997). Namely, the left- or right-eye specific subregions are defined as the domains in which model neurons are classified into the top 20% of total number of neurons. The border subregions of ocular dominance are defined as

We used a model visual cortex that consisted of two rectangular blocks sandwiched by two curved blocks in order to mimic the flat and curved parts of the visual cortex, respectively (Fig. 1a,b). The depth of the model cortex was 30 voxels. When we assume the depth to be 2 mm,

;,,

 

*j k*

, ,

 

*k j out*

*O*

and that for the preferred direction, <sup>222</sup> *pref pref pref pref*

*x j*

 

 

 

the domains in which neurons exhibit binocularity within 20%.

**2.2. Simulated representation of preferred orientation and direction** 

*y j*

*z j*

discontinuity sheets, respectively.

given by

, ,

 

*k*

, , *jjj xyz* in the model cortex. Gradient vector components of any given function

*x h*

*y h*

*z h*

Here we suppose that a global stimulus like an oriented grating stimulus moving in the direction is presented to both eyes. The direction tuning curve of model cortical neuron *j* is defined by

$$\mathcal{L}\_{\boldsymbol{j}}^{dir}\left(\boldsymbol{\theta}\right) = \sum\_{k,\mu} \eta\_{\boldsymbol{j};k,\boldsymbol{\theta},\mu}^{out}.\tag{10}$$

The preferred direction of the neuron *j* is determined when *dir j* takes the maximum at *pref j* . The orientation tuning curve of model cortical neuron *j* is defined by the sum of responses to opposite directions orthogonal to the stimulus orientation as follows:

$$\mathcal{L}\_{j}^{out}(\boldsymbol{\phi}) = \frac{1}{2} \sum\_{\mathbf{k},\mu} \left( \eta\_{j;\mathbf{k},\boldsymbol{\phi}-\pi/2,\mu}^{out} + \eta\_{j;\mathbf{k},\boldsymbol{\phi}+\pi/2,\mu}^{out} \right). \tag{11}$$

The preferred orientation of this neuron is determined when the orientation tuning curve ( ) *ori j* takes the maximum at *pref j* . The ocular dominance of neuronal responses is defined by the contrast form of responses to the left- and right-eye stimuli as follows:

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 347

$$\mathbf{O}\_{j} = \frac{\sum\_{k, \theta, \mu} \mu \eta\_{j; k, \theta, \mu}^{\mathrm{out}}}{\sum\_{k, \theta, \mu} \eta\_{j; k, \theta, \mu}^{\mathrm{out}}}. \tag{12}$$

To analyze orientation and direction singularities, we calculate an absolute value of the gradient vector for the preferred orientation, <sup>222</sup> *pref pref pref pref j xj yj zj* , and that for the preferred direction, <sup>222</sup> *pref pref pref pref j xj yj zj* , at position , , *jjj xyz* in the model cortex. Gradient vector components of any given function *<sup>j</sup>* are given by

346 Visual Cortex – Current Status and Perspectives

1 2 1

*2.1.4. Analyses of cortical feature representations* 

Therefore, the output neuronal response is expressed by

*T T*

13 12 *inner t T*

anisotropic cortical interaction.

*z*

direction

is defined by

*pref j* 

( ) *ori j* 

of the model visual cortex when the simulation is carried out to update features sampled randomly from the entire model cortex. So, we carried out the simulation according to the following schedule of spatial simulated annealing that we call hereafter, which assists the formation of iso-orientation domains continuously extending from layer 2 to layer 6. For simulation step *t* in the interval of 1 1 *t T* , simulation was performed only in the middle layer whose upper and lower boundaries were located at 13 and 18 voxels from the bottom of the model cortex. For 1 2 *T tT* , the inner boundary *inner <sup>z</sup>* and the outer boundary *outer <sup>z</sup>* of the simulation range in the depth direction was gradually changed as

2 1

. For 2 *T t* , simulation was carried

*T T* 

out in all model visual cortex. Here, one simulation step is defined as the trial numbers of updating features by the total number of voxels within which simulation is conducted. That is, for 1 simulation step, each voxel is tried to be updated, on average, once. Parameters 1 *T* and <sup>2</sup> *T* were set at 100 and 350 simulation steps. Thus, columnar structures of visual features are likely formed owing to the spatial simulated annealing, even if we do not assume

An output response of neuron *j* to the presentation of visual images through either eye is given by the integration of evoked activities at nearby neurons through the dendrite.

;,, ; ;, , , ; ,

Here we suppose that a global stimulus like an oriented grating stimulus moving in the

 ;,, , . *dir out j j k k*

. The orientation tuning curve of model cortical neuron *j* is defined by the sum of

<sup>1</sup> ( ) . <sup>2</sup>

 

The preferred orientation of this neuron is determined when the orientation tuning curve

 

*V R*

 . *out j k jj j l l k*

 

 

is presented to both eyes. The direction tuning curve of model cortical neuron *j*

 

; , 2, ; , 2,

 

 

(9)

(10)

takes the maximum at

as follows:

*j* 

(11)

. The ocular dominance of neuronal responses is

', ,

*j l*

 

The preferred direction of the neuron *j* is determined when *dir*

takes the maximum at *pref*

responses to opposite directions orthogonal to the stimulus orientation

,

 

*k*

*j* 

*ori out out j j k j k*

defined by the contrast form of responses to the left- and right-eye stimuli as follows:

, and <sup>1</sup>

*z*

18 12 *outer t T*

$$\begin{split} \boldsymbol{\hat{\sigma}}\_{x}\boldsymbol{\nu}\_{\boldsymbol{j}} &= \frac{\boldsymbol{\hat{\sigma}}\boldsymbol{\nu}\_{\boldsymbol{j}}}{\boldsymbol{\hat{\sigma}}\boldsymbol{x}} = \frac{\boldsymbol{\nu}\left(\mathbf{x}\_{j} + \boldsymbol{h}, \mathbf{y}\_{j}, \mathbf{z}\_{j}\right) - \boldsymbol{\nu}\left(\mathbf{x}\_{j} - \boldsymbol{h}, \mathbf{y}\_{j}, \mathbf{z}\_{j}\right)}{2\boldsymbol{h}} \\ \boldsymbol{\hat{\sigma}}\_{y}\boldsymbol{\nu}\_{\boldsymbol{j}} &= \frac{\boldsymbol{\hat{\sigma}}\boldsymbol{\nu}\_{\boldsymbol{j}}}{\boldsymbol{\hat{\sigma}}\boldsymbol{y}} = \frac{\boldsymbol{\nu}\left(\mathbf{x}\_{j}, \mathbf{y}\_{j} + \boldsymbol{h}, \mathbf{z}\_{j}\right) - \boldsymbol{\nu}\left(\mathbf{x}\_{j}, \mathbf{y}\_{j} - \boldsymbol{h}, \mathbf{z}\_{j}\right)}{2\boldsymbol{h}}}{2\boldsymbol{h}}, \\ \boldsymbol{\hat{\sigma}}\_{z}\boldsymbol{\nu}\_{\boldsymbol{j}} &= \frac{\boldsymbol{\hat{\sigma}}\boldsymbol{\nu}\_{\boldsymbol{j}}}{\boldsymbol{\hat{\sigma}}\boldsymbol{z}} = \frac{\boldsymbol{\nu}\left(\mathbf{x}\_{j}, \mathbf{y}\_{j}, \mathbf{z}\_{j} + \boldsymbol{h}\right) - \boldsymbol{\nu}\left(\mathbf{x}\_{j}, \mathbf{y}\_{j}, \mathbf{z}\_{j} - \boldsymbol{h}\right)}{2\boldsymbol{h}} \end{split} \tag{13}$$

where *h* indicates the distance between nearest neighbor model neurons, which is the size of the voxel corresponding 66μm. Plotting grey spots at positions where absolute values of orientation gradient vectors are larger than 0.01 rad/μm and those of direction gradient vectors are larger than 0.02 rad/μm, we obtain orientation-singularity lines and directiondiscontinuity sheets, respectively.

The positions of orientation pinwheel centers along the model cortical surface are determined automatically as the points around which the circular integration of the orientation difference amounts to just ±*π*, where the circular integration is taken in the counterclockwise direction. The counterclockwise or clockwise pinwheel centers are defined by the sign of the integral. Thus-defined pinwheel centers coincided with the position of high-gradient of preferred orientations along the cortical surface. The relative number of pinwheel centers within ocular dominance subregions is analyzed according to the previously introduced method (Hübener et al. 1997). Namely, the left- or right-eye specific subregions are defined as the domains in which model neurons are classified into the top 20% of total number of neurons. The border subregions of ocular dominance are defined as the domains in which neurons exhibit binocularity within 20%.

#### **2.2. Simulated representation of preferred orientation and direction**

We used a model visual cortex that consisted of two rectangular blocks sandwiched by two curved blocks in order to mimic the flat and curved parts of the visual cortex, respectively (Fig. 1a,b). The depth of the model cortex was 30 voxels. When we assume the depth to be 2 mm, one voxel size corresponds to 66 μm. The rectangular blocks simulate flat parts between the lateral sulcus and the crown of the lateral gyrus, whereas the curved blocks simulate the crown of the lateral gyrus. To minimize the size effect, we imposed a periodic boundary condition on the model visual cortex. In the computer simulation, we assumed the standard deviation of the dendritic arbor of cortical neurons to be 2 voxels, which corresponds to 132 μm. This implies a diameter of 264 μm, which falls in a plausible range for the tangential diameter of stellate cell dendritic arborization and pyramidal cell basal dendritic arborization in the visual cortex (290 ± 88 (SD) μm, n = 12; estimated from the data shown in Sholl, 1953).

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 349

Preferred directions were also arranged almost continuously along the cortical surface, except along discontinuity lines across which the preferred direction changed by 180° (Fig. 1b). Such direction-discontinuity lines (Kim et al., 1999) or direction fractures (Kisvarday et al., 2001; Swindale et al., 2003) are indicated by white lines. They were terminated or branched at pinwheel centers shown as black dots in Fig. 1b, which is consistent with a previous experimental finding (Kim et al., 1999; Weliky et al., 1996) and a theoretical prediction (Tanaka, 1995, 1997). Like iso-orientation domains, iso-direction domains tended to extend vertically in the flat part of the model cortex. However, there were occasions in

To more clearly visualize orientation representation particularly along the cortical depth, we illustrated iso-orientation lines (Fig. 1c), where the orientation interval between adjacent lines was 15°. In the flat part of the cortex, iso-orientation lines tended to penetrate throughout the cortical depth. In contrast, in the curved part, some iso-orientation lines exhibited V-shape, although some connected the cortical surface with the bottom of the

The present model also demonstrates the emergence of ocular dominance patches mainly in the middle layer and an orderly retinotopic arrangement of the receptive field center (for

We illustrated high-gradient regions for the preferred orientation and direction. The highorientation-gradient regions appeared as thin rods, shown by gray rods in Figs. 2a and b. Note that these rods passed through pinwheel centers at any given layer. In the flat part of the cortex, 82 % of high-orientation-gradient rods tended to stand straight, and the other small percent (18 %) were V-shaped lines or hairpins. From visual inspection, all rods that reached the cortical surface intersected perpendicularly to the cortical surface (Fig. 2a). In contrast, in the curved part of the cortex, 34 % of high-orientation-gradient rods appeared to be hairpins whose ends were located on the cortical surface, whereas the other percent of rods (66 %) were almost straight lines connecting the cortical surface with the bottom of the cortex (Fig. 2b). The appearance of the V-shaped orientation singularity lines is due to the fact that clockwise and counterclockwise orientation pinwheel centers in a plane parallel to the cortical surface get closer and finally they are merged to vanish, as the plane moves

On the other hand, high-direction-gradient regions appeared as thin, curved and distorted sheets, shown by gray sheets in Figs. 2c and d. Figure 2c (the top view in Fig. 2a) shows the high-direction-gradient sheets in the flat part of the model visual cortex, whereas Fig. 2d illustrates the side view in Fig. 2b, showing the high-direction-gradient sheets in the curved part. From visual inspection, the high-direction-gradient sheets seemed to intersect with the cortical surface or the bottom of the cortex at right angles. The intersections formed the direction-discontinuity lines on the cortical surface, which are indicated by blue lines in

which the preferred direction was reversed somewhere along the cortical depth.

**2.3. Simulated 3D structures of orientation and direction singularities** 

cortex, forming wedge-like orientation columns.

more information, see Tanaka et al., 2011).

down from the surface to deeper layers.

Figs. 2c and d (corresponding to the white lines in Fig. 1b).

**Figure 1.** Three-dimensional self-organized representations.

The computer simulation formed a 3D structure of orderly arrangements of preferred orientations, which are shown by different colors (Fig. 1a). Along the model cortical surface, either in the flat or curved parts, the preferred orientation changed continuously except at singularity points called pinwheel centers, around which the preferred orientation changes in a circular fashion (Bonhoeffer & Grinvald, 1991). The preferred orientation did not change much in the depth direction of the flat parts of the model cortex, which can be seen in the sidewall of the model cortex. This indicates that preferred orientations are arranged in a columnar fashion in the flat cortical parts. On the other hand, in the curved parts of the model cortex, preferred orientations were arranged in a wedge-like structure, where the width of orientation columns became narrower from the superficial to deeper layers. It should be noted that tips of some wedge-like columns did not reach the bottom of the model visual cortex, which corresponds to the boundary between the grey and white matter.

Preferred directions were also arranged almost continuously along the cortical surface, except along discontinuity lines across which the preferred direction changed by 180° (Fig. 1b). Such direction-discontinuity lines (Kim et al., 1999) or direction fractures (Kisvarday et al., 2001; Swindale et al., 2003) are indicated by white lines. They were terminated or branched at pinwheel centers shown as black dots in Fig. 1b, which is consistent with a previous experimental finding (Kim et al., 1999; Weliky et al., 1996) and a theoretical prediction (Tanaka, 1995, 1997). Like iso-orientation domains, iso-direction domains tended to extend vertically in the flat part of the model cortex. However, there were occasions in which the preferred direction was reversed somewhere along the cortical depth.

348 Visual Cortex – Current Status and Perspectives

**Figure 1.** Three-dimensional self-organized representations.

one voxel size corresponds to 66 μm. The rectangular blocks simulate flat parts between the lateral sulcus and the crown of the lateral gyrus, whereas the curved blocks simulate the crown of the lateral gyrus. To minimize the size effect, we imposed a periodic boundary condition on the model visual cortex. In the computer simulation, we assumed the standard deviation of the dendritic arbor of cortical neurons to be 2 voxels, which corresponds to 132 μm. This implies a diameter of 264 μm, which falls in a plausible range for the tangential diameter of stellate cell dendritic arborization and pyramidal cell basal dendritic arborization in the visual cortex (290 ± 88 (SD) μm, n = 12; estimated from the data shown in Sholl, 1953).

The computer simulation formed a 3D structure of orderly arrangements of preferred orientations, which are shown by different colors (Fig. 1a). Along the model cortical surface, either in the flat or curved parts, the preferred orientation changed continuously except at singularity points called pinwheel centers, around which the preferred orientation changes in a circular fashion (Bonhoeffer & Grinvald, 1991). The preferred orientation did not change much in the depth direction of the flat parts of the model cortex, which can be seen in the sidewall of the model cortex. This indicates that preferred orientations are arranged in a columnar fashion in the flat cortical parts. On the other hand, in the curved parts of the model cortex, preferred orientations were arranged in a wedge-like structure, where the width of orientation columns became narrower from the superficial to deeper layers. It should be noted that tips of some wedge-like columns did not reach the bottom of the model visual cortex, which corresponds to the boundary between the grey and white matter.

To more clearly visualize orientation representation particularly along the cortical depth, we illustrated iso-orientation lines (Fig. 1c), where the orientation interval between adjacent lines was 15°. In the flat part of the cortex, iso-orientation lines tended to penetrate throughout the cortical depth. In contrast, in the curved part, some iso-orientation lines exhibited V-shape, although some connected the cortical surface with the bottom of the cortex, forming wedge-like orientation columns.

The present model also demonstrates the emergence of ocular dominance patches mainly in the middle layer and an orderly retinotopic arrangement of the receptive field center (for more information, see Tanaka et al., 2011).

## **2.3. Simulated 3D structures of orientation and direction singularities**

We illustrated high-gradient regions for the preferred orientation and direction. The highorientation-gradient regions appeared as thin rods, shown by gray rods in Figs. 2a and b. Note that these rods passed through pinwheel centers at any given layer. In the flat part of the cortex, 82 % of high-orientation-gradient rods tended to stand straight, and the other small percent (18 %) were V-shaped lines or hairpins. From visual inspection, all rods that reached the cortical surface intersected perpendicularly to the cortical surface (Fig. 2a). In contrast, in the curved part of the cortex, 34 % of high-orientation-gradient rods appeared to be hairpins whose ends were located on the cortical surface, whereas the other percent of rods (66 %) were almost straight lines connecting the cortical surface with the bottom of the cortex (Fig. 2b). The appearance of the V-shaped orientation singularity lines is due to the fact that clockwise and counterclockwise orientation pinwheel centers in a plane parallel to the cortical surface get closer and finally they are merged to vanish, as the plane moves down from the surface to deeper layers.

On the other hand, high-direction-gradient regions appeared as thin, curved and distorted sheets, shown by gray sheets in Figs. 2c and d. Figure 2c (the top view in Fig. 2a) shows the high-direction-gradient sheets in the flat part of the model visual cortex, whereas Fig. 2d illustrates the side view in Fig. 2b, showing the high-direction-gradient sheets in the curved part. From visual inspection, the high-direction-gradient sheets seemed to intersect with the cortical surface or the bottom of the cortex at right angles. The intersections formed the direction-discontinuity lines on the cortical surface, which are indicated by blue lines in Figs. 2c and d (corresponding to the white lines in Fig. 1b).

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 351

preferred orientation changes moderately in all traces. The preferred direction in most traces showed similar changes in the preferred orientation either in the curved or flat part. Some of them, however, showed abrupt changes by 180° in the middle of the cortical depth. More reversals of preferred directions were found in the deeper layers in the curved part. We analyzed the depth-dependent probabilities to hit direction reversals in the flat and curved parts of the cortex, by sampling 14336 traces in the flat part and 14476 traces in the curved part, discarding the other traces in the transition regions between the flat and curved parts. If the direction-discontinuity sheets are flat planes, the probability to hit direction reversals is independent of the depth. However, direction reversals tended to occur more frequently midway through the cortical depth than near the cortical surface or the bottom of the cortex. This tendency reflects that direction-discontinuity sheets were wavy. The small probability to hit direction reversals near the cortical surface and the bottom of the cortex indicates that the direction-discontinuity sheets tended to intersect the cortical surface and the bottom of the cortex at right angles. In contrast, in the curved part, the preferred direction reversed more frequently in deeper layers than in superficial layers. The direction reversal in deeper layers appeared twice to three times more frequently in the curved part than in the flat part.

**2.4. Multi-slice fMRI imaging of 3D orientation representation** 

were performed for signal averaging.

To compare simulated results with the feature representation in the cat visual cortex, highresolution fMRI was performed on cat visual cortex at 9.4 T (Fukuda et al., 2006; Moon et al., 2007). The cat was anesthetized with isoflurane (0.8 – 1.0%) and immobilized with pancuronium bromide (0.2 mg kg-1 hr-1, i.v.). The imaging positions were selected in the region of dorsal surface and smaller surface veins, based on high-resolution 3D anatomical images. fMRI data were acquired using the multi-slice 2D gradient echo planner imaging sequence with the following parameters: TR = 2.0 s, TE = 10 ms, matrix = 96 × 96, and FOV = 2 × 2 cm2, after an intravascular bolus injection of a dextran-coated monocrystalline iron oxide nanoparticles (MION) contrast agent (20 mg Fe kg-1 body weight). Slice thickness of 500 m was chosen to achieve sufficient sensitivity, and an inter-slice distance of 375 m was used to improve nominal resolution along a slice direction. Visual stimulation was given binocularly. Eight different orientations with high-contrast square-wave full-field moving gratings (0.15 cpd, 2 Hz, movement direction reversal per 0.5 s) were presented sequentially (22.5° angle increments, 10 s per angle) during the one cycle of 80 s. One stimulation cycle was repeated ten times continuously (i.e., total 800 s per run). Twenty runs

In the data analysis, first, we obtained single-condition maps for the 8 stimulus orientations at 8 slices according to Kalatsky & Stryker's method (Kalatsky & Stryker, 2003). We embedded each single-condition map into a rectangular solid region of 192 × 192 × 25 voxels, each of which was the size of 104 × 104 × 125 μm3. (For more information, see Supporting Figure 1 of Tanaka et al., 2011). Then we applied the 2D Gaussian filtering with the standard deviation of 193 μm to the single-condition maps for 8 stimulus orientations at each slice. Then we applied the same filtering to these single-condition maps in the depth direction. The preferred orientation at each voxel was determined by the vector sum method

**Figure 2.** Orientation and direction singularities in the model cortex.

The high-direction-gradient sheets were delineated by the high-orientation-gradient rods (red rods) or the upper and lower cortical surfaces. This 3D relationship is a natural extension of the 2D relationship between pinwheel centers and direction-discontinuity lines (Figs. 1a and b). Hereafter, we refer to high-orientation-gradient rods as orientationsingularity lines and high-direction-gradient sheets as direction-discontinuity sheets. As seen in Fig. 2d, when we move radially from the cortical surface to the bottom of the cortex, the direction-discontinuity sheets became crowded. Even in the flat part (Fig. 2c), directiondiscontinuity sheets covered a non-negligible area of the cortex when we see the cortex from the top. Such patterns indicate that when an electrode penetrates vertically into the visual cortex in electrophysiological recording, the preferred direction along the electrode track can reverse somewhere in the middle of the cortical depth.

To examine how the preferred orientation and direction changed in the flat and curved parts of the model cortex, we sampled 40 traces of preferred orientations and preferred directions along the cortical depth from the surface to the bottom of the cortex perpendicularly to the cortical surface. Most traces in either the curved or flat parts showed gradual changes in the preferred orientation deviating from the orientations at the cortical surface. Some traces showed large changes of preferred orientations in the middle of the cortical depth in the curved part. The mean amplitudes of changes in the preferred orientation along the depth were 35.1° and 30.5°, and the standard deviations of the amplitudes of changes in the preferred orientation were 35.0° and 16.0°, in the curved and flat parts, respectively. These values indicate that in the curved cortex, the preferred orientation changes drastically in some traces but it is rather constant in the other traces, whereas in the flat cortex, the preferred orientation changes moderately in all traces. The preferred direction in most traces showed similar changes in the preferred orientation either in the curved or flat part. Some of them, however, showed abrupt changes by 180° in the middle of the cortical depth. More reversals of preferred directions were found in the deeper layers in the curved part. We analyzed the depth-dependent probabilities to hit direction reversals in the flat and curved parts of the cortex, by sampling 14336 traces in the flat part and 14476 traces in the curved part, discarding the other traces in the transition regions between the flat and curved parts. If the direction-discontinuity sheets are flat planes, the probability to hit direction reversals is independent of the depth. However, direction reversals tended to occur more frequently midway through the cortical depth than near the cortical surface or the bottom of the cortex. This tendency reflects that direction-discontinuity sheets were wavy. The small probability to hit direction reversals near the cortical surface and the bottom of the cortex indicates that the direction-discontinuity sheets tended to intersect the cortical surface and the bottom of the cortex at right angles. In contrast, in the curved part, the preferred direction reversed more frequently in deeper layers than in superficial layers. The direction reversal in deeper layers appeared twice to three times more frequently in the curved part than in the flat part.

### **2.4. Multi-slice fMRI imaging of 3D orientation representation**

350 Visual Cortex – Current Status and Perspectives

**Figure 2.** Orientation and direction singularities in the model cortex.

reverse somewhere in the middle of the cortical depth.

The high-direction-gradient sheets were delineated by the high-orientation-gradient rods (red rods) or the upper and lower cortical surfaces. This 3D relationship is a natural extension of the 2D relationship between pinwheel centers and direction-discontinuity lines (Figs. 1a and b). Hereafter, we refer to high-orientation-gradient rods as orientationsingularity lines and high-direction-gradient sheets as direction-discontinuity sheets. As seen in Fig. 2d, when we move radially from the cortical surface to the bottom of the cortex, the direction-discontinuity sheets became crowded. Even in the flat part (Fig. 2c), directiondiscontinuity sheets covered a non-negligible area of the cortex when we see the cortex from the top. Such patterns indicate that when an electrode penetrates vertically into the visual cortex in electrophysiological recording, the preferred direction along the electrode track can

To examine how the preferred orientation and direction changed in the flat and curved parts of the model cortex, we sampled 40 traces of preferred orientations and preferred directions along the cortical depth from the surface to the bottom of the cortex perpendicularly to the cortical surface. Most traces in either the curved or flat parts showed gradual changes in the preferred orientation deviating from the orientations at the cortical surface. Some traces showed large changes of preferred orientations in the middle of the cortical depth in the curved part. The mean amplitudes of changes in the preferred orientation along the depth were 35.1° and 30.5°, and the standard deviations of the amplitudes of changes in the preferred orientation were 35.0° and 16.0°, in the curved and flat parts, respectively. These values indicate that in the curved cortex, the preferred orientation changes drastically in some traces but it is rather constant in the other traces, whereas in the flat cortex, the To compare simulated results with the feature representation in the cat visual cortex, highresolution fMRI was performed on cat visual cortex at 9.4 T (Fukuda et al., 2006; Moon et al., 2007). The cat was anesthetized with isoflurane (0.8 – 1.0%) and immobilized with pancuronium bromide (0.2 mg kg-1 hr-1, i.v.). The imaging positions were selected in the region of dorsal surface and smaller surface veins, based on high-resolution 3D anatomical images. fMRI data were acquired using the multi-slice 2D gradient echo planner imaging sequence with the following parameters: TR = 2.0 s, TE = 10 ms, matrix = 96 × 96, and FOV = 2 × 2 cm2, after an intravascular bolus injection of a dextran-coated monocrystalline iron oxide nanoparticles (MION) contrast agent (20 mg Fe kg-1 body weight). Slice thickness of 500 m was chosen to achieve sufficient sensitivity, and an inter-slice distance of 375 m was used to improve nominal resolution along a slice direction. Visual stimulation was given binocularly. Eight different orientations with high-contrast square-wave full-field moving gratings (0.15 cpd, 2 Hz, movement direction reversal per 0.5 s) were presented sequentially (22.5° angle increments, 10 s per angle) during the one cycle of 80 s. One stimulation cycle was repeated ten times continuously (i.e., total 800 s per run). Twenty runs were performed for signal averaging.

In the data analysis, first, we obtained single-condition maps for the 8 stimulus orientations at 8 slices according to Kalatsky & Stryker's method (Kalatsky & Stryker, 2003). We embedded each single-condition map into a rectangular solid region of 192 × 192 × 25 voxels, each of which was the size of 104 × 104 × 125 μm3. (For more information, see Supporting Figure 1 of Tanaka et al., 2011). Then we applied the 2D Gaussian filtering with the standard deviation of 193 μm to the single-condition maps for 8 stimulus orientations at each slice. Then we applied the same filtering to these single-condition maps in the depth direction. The preferred orientation at each voxel was determined by the vector sum method (Bonhoeffer & Grinvald, 1991), which is based on the Fourier analysis in the circular symmetric orientation dimension. Next, we calculated the orientation gradient at each voxel using Eq. (13) to show the region of orientation singularities. When we visualized highorientation-gradient regions, we used the same threshold value as in the model.

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 353

imaging rectangular parallelepiped. The white scale bar placed at the bottom of slice #0 indicates 1 mm. Each map shows the characteristic spatial clustering of preferred orientations. Figure 4 shows orientation representation and orientation-singularity lines in the 125-μmthick slices, where the abbreviations for the cortical coordinates, M, L, D and V indicate, respectively, medial, lateral, dorsal and ventral, and all black scale bars indicate 1mm. In Fig. 4 in a saggital section containing the white matter, the orientation columns tended to terminate at the bottom of the grey matter at right angles with the boundary between the grey and white matter. The regions of high orientation gradient in the same section appeared as nearly straight rods, which terminated at pinwheel centers on the cortical surface (Fig. 4b). Figure 4c shows the lateral view of orientation representation in the section more medial to the section shown in Fig. 4a, which did not contain the white matter. Around the horizontal dotted line in this section, which corresponded to the bottom of the curved cortex in the medial region of recording, iso-orientation domains were not aligned vertically, different from those in Fig. 4a. Orientation-singularity lines were fractioned around there (Fig. 4d). These features imply that iso-orientation domains and singularity

lines tend to run along the radial axes of the gyrus.

**Figure 4.** Orientation representations and high orientation gradients in typical sections.

feature, the orientation-singularity lines appeared as hair-pins (Figs. 4f and h).

In the coronal sections around the gyrus, preferred orientations were arranged in wedgelike columns rather than straight columns (Figs. 4e and g), whereas iso-orientation domains ran orthogonal to the white matter closer to the flat parts of the cortex (white arrow heads in Fig. 4e). Tips of some wedges representing single orientations (black arrow heads in Figs. 4e and g) did not reach the white matter, which indicates that orientation preferences can differ between the superficial and deep layers along the radial axes of the gyrus. Related to this

## **2.5. Orientation representation reconstructed by high-resolution fMRI**

Figures 3a and b, respectively, are top and coronal views reconstructed from a 3D venogram. Abbreviations of A, P, L and R for the cortical coordinate in Fig. 3a indicate anterior, posterior, left and right, respectively. Venous vessels were enhanced in the dark intensity dots and lines from tissues. The disk region of a 1.5-cm diameter with a 3.125-mm thickness surrounded by the red lines was selected for imaging. fMRI data were acquired using the multi-slice 2D gradient echo planar imaging sequence in 2 × 2 cm2 around the lateral gyrus including areas 17 and 18 (Fig. 3a), following an intravascular bolus injection of MION. Eight 0.5-mm-thick slices were obtained from a 3.125-mm-thick slab (center-to-center distance of neighboring slices = 0.375 mm) (Fig. 3b).

**Figure 3.** Multi-slice high-resolution fMRI of cat visual cortex.

Eight single-condition maps responding to binocular full-field gratings were obtained for each slice. Then, 8 orientation polar maps were calculated for different depths in the rectangular region, where color and brightness indicate the preferred orientation and orientation selectivity, respectively (Fig. 3c). The slice number is assigned from the bottom to the top in the imaging rectangular parallelepiped. The white scale bar placed at the bottom of slice #0 indicates 1 mm. Each map shows the characteristic spatial clustering of preferred orientations.

352 Visual Cortex – Current Status and Perspectives

distance of neighboring slices = 0.375 mm) (Fig. 3b).

**Figure 3.** Multi-slice high-resolution fMRI of cat visual cortex.

Eight single-condition maps responding to binocular full-field gratings were obtained for each slice. Then, 8 orientation polar maps were calculated for different depths in the rectangular region, where color and brightness indicate the preferred orientation and orientation selectivity, respectively (Fig. 3c). The slice number is assigned from the bottom to the top in the

(Bonhoeffer & Grinvald, 1991), which is based on the Fourier analysis in the circular symmetric orientation dimension. Next, we calculated the orientation gradient at each voxel using Eq. (13) to show the region of orientation singularities. When we visualized high-

Figures 3a and b, respectively, are top and coronal views reconstructed from a 3D venogram. Abbreviations of A, P, L and R for the cortical coordinate in Fig. 3a indicate anterior, posterior, left and right, respectively. Venous vessels were enhanced in the dark intensity dots and lines from tissues. The disk region of a 1.5-cm diameter with a 3.125-mm thickness surrounded by the red lines was selected for imaging. fMRI data were acquired using the multi-slice 2D gradient echo planar imaging sequence in 2 × 2 cm2 around the lateral gyrus including areas 17 and 18 (Fig. 3a), following an intravascular bolus injection of MION. Eight 0.5-mm-thick slices were obtained from a 3.125-mm-thick slab (center-to-center

orientation-gradient regions, we used the same threshold value as in the model.

**2.5. Orientation representation reconstructed by high-resolution fMRI** 

Figure 4 shows orientation representation and orientation-singularity lines in the 125-μmthick slices, where the abbreviations for the cortical coordinates, M, L, D and V indicate, respectively, medial, lateral, dorsal and ventral, and all black scale bars indicate 1mm. In Fig. 4 in a saggital section containing the white matter, the orientation columns tended to terminate at the bottom of the grey matter at right angles with the boundary between the grey and white matter. The regions of high orientation gradient in the same section appeared as nearly straight rods, which terminated at pinwheel centers on the cortical surface (Fig. 4b). Figure 4c shows the lateral view of orientation representation in the section more medial to the section shown in Fig. 4a, which did not contain the white matter. Around the horizontal dotted line in this section, which corresponded to the bottom of the curved cortex in the medial region of recording, iso-orientation domains were not aligned vertically, different from those in Fig. 4a. Orientation-singularity lines were fractioned around there (Fig. 4d). These features imply that iso-orientation domains and singularity lines tend to run along the radial axes of the gyrus.

**Figure 4.** Orientation representations and high orientation gradients in typical sections.

In the coronal sections around the gyrus, preferred orientations were arranged in wedgelike columns rather than straight columns (Figs. 4e and g), whereas iso-orientation domains ran orthogonal to the white matter closer to the flat parts of the cortex (white arrow heads in Fig. 4e). Tips of some wedges representing single orientations (black arrow heads in Figs. 4e and g) did not reach the white matter, which indicates that orientation preferences can differ between the superficial and deep layers along the radial axes of the gyrus. Related to this feature, the orientation-singularity lines appeared as hair-pins (Figs. 4f and h).

To visualize the orientation columns around the curved and flat cortices more clearly, we illustrated iso-orientation lines in 3 slices of the coronal section and in 1 slice of the sagittal section, where the orientation interval between adjacent contour lines was set at 15° in Fig. 5. In the curved cortex, although some iso-orientation lines tended to reach the white matter, many lines were hairpins or converged to some points in the grey matter, and hence did not connect the cortical surface with the white matter (Figs. 5a-c). These characteristic features agree with the simulated results in the curved part of the model cortex (Fig. 1c). On the other hand, isoorientation lines tended to connect the cortical surface and the white matter straightly in the flat cortex as shown in the dotted square of Fig. 5d. Interestingly, even in the flat cortex, some isoorientation lines were hairpins or converged to points in the grey matter. This indicates that all orientation columns in the flat cortex are not necessarily straight columns.

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 355

average penetration index was smaller in the curved part than in the flat part indicates that orientation columns more often terminate somewhere in the cortical depth in the curved

On the other hand, in the simulated results, the average penetration index was 0.99 in the flat part of the cortex, and 0.89 in the curved part. It is quite likely that these values are larger than those obtained from the experiment, because detailed connectivity among different neurons and its complexity were omitted in the model. However, these two values again indicate that orientation columns in the curved cortex more frequently terminate somewhere in the cortical depth than do those in flat cortex. Taken together, the experiment and theory show that the cortical curvature partially deforms columnar organization and

The cerebral cortex is packed into the skull and folded in a complex manner. Accordingly, most parts of the cerebral cortex are curved. It would be expected that sensory feature representation is constrained by the curvature of the cortex. Schematic pictures of columnar organization for feature representation shown in many textbooks (for example, see Kandel et al., 2000) may be too simple, particularly in the curved part of the gyrus. Hubel and Wiesel (1962) suggested that wedge-like columns appear in the apical segment of the postlateral gyrus because the columns are parallel to the radial fiber bundles and perpendicular to cortical layers. The present simulation and fMRI data demonstrated that orientation columns are likely to appear as wedge-like columns rather than straight columns in curved

More interestingly, the columns do not necessarily extend from the cortical surface to the white matter. Related to this property, the present studies showed that orientation singularities were not necessarily straight lines penetrating from the cortical surface to the white matter. Some of them appear as hairpins, as shown in the simulation (Figs. 2a and b) and in the fMRI data from cat visual cortex (Figs. 4f and h). Since the topological constraint on the preferred orientation and direction should be valid in the bulk of the cortex either flat or curved, direction-discontinuity sheets are delimited by orientation-singularity lines. The original view of the configuration of orientation columns as the stacked-slab arrangement (Hubel et al., 1977) has been revised as a view of a pinwheel arrangement of preferred orientations (Bonhoeffer & Grinvald, 1991), retaining the concept of organization of straight columns. The present study suggests that columnar organization may be more complex than expected from the conventional hypercolumn picture: orientation columns are distorted to reconcile with the global curvature of the visual cortex. The wedge-like shape of an orientation column and the interruption of columns in the deeper layers can release the tension induced by the columnar organization in curved grey matter. Our model indicates that such a structure emerges from the competition between the tendencies of periodic arrangement and radial alignment of orientation representation in a curved cortex. Because a large part of area 17 is located near the crown of the gyrus, anatomical minicolumns arranged along the radial fiber bundle may functionally represent different orientations in

cortex, consistent with visual inspection of Figs. 4 and 5.

disrupts some columns in the middle of the cortical depth.

**2.6. Effects of curvature of visual cortex** 

parts of the cortex.

**Figure 5.** Iso-orientation line maps in 4 typical sections.

When the planes of the slice sections are not parallel to the straight columns that penetrate throughout the grey matter, they may appear to terminate in the middle of the cortical depth. Also, when orientation columns are wavy, even if penetrating throughout the grey matter, they may appear to terminate in the middle of the cortical depth. To examine quantitatively whether orientation columns continuously extend throughout the cortical depth at least within the imaged region, we defined the penetration index: It takes 1 when we can find a continuous path from the surface down to the white matter, keeping the same orientation within the range of 22.°, and otherwise, 0. The penetration indices were averaged in flat or curved parts both for the experimental and theoretical results. To estimate average penetration indices, we used 14336 traces (= 56 × 128 × 2 pixels) in the up- and downside of the flat parts and 14336 traces (≅ [72 × π/4] × 128 × 2 pixels) in the most lateral curved parts in the simulated result, and 636 and 830 traces in the flat and curved parts, respectively, in the experimental result. The average penetration index estimated from the fMRI data was 0.87 for the flat cortex, and 0.78 for the curved cortex. These values indicate that not all orientation columns continued from the cortical surface to the white matter. However, some orientation columns reached the white matter, even if they may be wavy. The fact that the average penetration index was smaller in the curved part than in the flat part indicates that orientation columns more often terminate somewhere in the cortical depth in the curved cortex, consistent with visual inspection of Figs. 4 and 5.

On the other hand, in the simulated results, the average penetration index was 0.99 in the flat part of the cortex, and 0.89 in the curved part. It is quite likely that these values are larger than those obtained from the experiment, because detailed connectivity among different neurons and its complexity were omitted in the model. However, these two values again indicate that orientation columns in the curved cortex more frequently terminate somewhere in the cortical depth than do those in flat cortex. Taken together, the experiment and theory show that the cortical curvature partially deforms columnar organization and disrupts some columns in the middle of the cortical depth.

### **2.6. Effects of curvature of visual cortex**

354 Visual Cortex – Current Status and Perspectives

To visualize the orientation columns around the curved and flat cortices more clearly, we illustrated iso-orientation lines in 3 slices of the coronal section and in 1 slice of the sagittal section, where the orientation interval between adjacent contour lines was set at 15° in Fig. 5. In the curved cortex, although some iso-orientation lines tended to reach the white matter, many lines were hairpins or converged to some points in the grey matter, and hence did not connect the cortical surface with the white matter (Figs. 5a-c). These characteristic features agree with the simulated results in the curved part of the model cortex (Fig. 1c). On the other hand, isoorientation lines tended to connect the cortical surface and the white matter straightly in the flat cortex as shown in the dotted square of Fig. 5d. Interestingly, even in the flat cortex, some isoorientation lines were hairpins or converged to points in the grey matter. This indicates that all

When the planes of the slice sections are not parallel to the straight columns that penetrate throughout the grey matter, they may appear to terminate in the middle of the cortical depth. Also, when orientation columns are wavy, even if penetrating throughout the grey matter, they may appear to terminate in the middle of the cortical depth. To examine quantitatively whether orientation columns continuously extend throughout the cortical depth at least within the imaged region, we defined the penetration index: It takes 1 when we can find a continuous path from the surface down to the white matter, keeping the same orientation within the range of 22.°, and otherwise, 0. The penetration indices were averaged in flat or curved parts both for the experimental and theoretical results. To estimate average penetration indices, we used 14336 traces (= 56 × 128 × 2 pixels) in the up- and downside of the flat parts and 14336 traces (≅ [72 × π/4] × 128 × 2 pixels) in the most lateral curved parts in the simulated result, and 636 and 830 traces in the flat and curved parts, respectively, in the experimental result. The average penetration index estimated from the fMRI data was 0.87 for the flat cortex, and 0.78 for the curved cortex. These values indicate that not all orientation columns continued from the cortical surface to the white matter. However, some orientation columns reached the white matter, even if they may be wavy. The fact that the

orientation columns in the flat cortex are not necessarily straight columns.

**Figure 5.** Iso-orientation line maps in 4 typical sections.

The cerebral cortex is packed into the skull and folded in a complex manner. Accordingly, most parts of the cerebral cortex are curved. It would be expected that sensory feature representation is constrained by the curvature of the cortex. Schematic pictures of columnar organization for feature representation shown in many textbooks (for example, see Kandel et al., 2000) may be too simple, particularly in the curved part of the gyrus. Hubel and Wiesel (1962) suggested that wedge-like columns appear in the apical segment of the postlateral gyrus because the columns are parallel to the radial fiber bundles and perpendicular to cortical layers. The present simulation and fMRI data demonstrated that orientation columns are likely to appear as wedge-like columns rather than straight columns in curved parts of the cortex.

More interestingly, the columns do not necessarily extend from the cortical surface to the white matter. Related to this property, the present studies showed that orientation singularities were not necessarily straight lines penetrating from the cortical surface to the white matter. Some of them appear as hairpins, as shown in the simulation (Figs. 2a and b) and in the fMRI data from cat visual cortex (Figs. 4f and h). Since the topological constraint on the preferred orientation and direction should be valid in the bulk of the cortex either flat or curved, direction-discontinuity sheets are delimited by orientation-singularity lines. The original view of the configuration of orientation columns as the stacked-slab arrangement (Hubel et al., 1977) has been revised as a view of a pinwheel arrangement of preferred orientations (Bonhoeffer & Grinvald, 1991), retaining the concept of organization of straight columns. The present study suggests that columnar organization may be more complex than expected from the conventional hypercolumn picture: orientation columns are distorted to reconcile with the global curvature of the visual cortex. The wedge-like shape of an orientation column and the interruption of columns in the deeper layers can release the tension induced by the columnar organization in curved grey matter. Our model indicates that such a structure emerges from the competition between the tendencies of periodic arrangement and radial alignment of orientation representation in a curved cortex. Because a large part of area 17 is located near the crown of the gyrus, anatomical minicolumns arranged along the radial fiber bundle may functionally represent different orientations in superficial layers and deep layers in area 17. Moreover, the present model predicts that the preferred direction can more frequently reverse in the deeper layers around the gyrus than in the flat cortex, as reported by Berman et al. (1987). To date, it is thought that the cortical information representation obeys a columnar organization rule. The present study suggests that careful examination of functional architecture is needed, because different pieces of information can be represented in an identical anatomical minicolumn at different layers, particularly around the crowns of gyri and perhaps at fundi.

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 357

were fixed on a stereotaxic apparatus and were artificially ventilated with a 60:40% mixture of N2O and O2 containing 0.5-1.0% isoflurane. Heart rate, end-tidal CO2 concentration, and rectal temperature were continuously monitored during surgery. A metal head holder for fixing the goggles and a metal chamber for optical imaging were cemented on the animal's skull using dental resin, and the skull and dura mater covering the recording area of the lateral gyrus were removed. The cranial window (17 mm × 12 mm) was positioned approximately from P5 to A12, spanning the midline. Next, the chamber was filled with 2% agar and sealed with a polyvinylidene chloride thin film and a plastic plate. Finally, the frame of the goggles was fixed to a head holder and the position of the goggles was

calibrated so that the cylindrical lenses covered the visual field as widely as possible.

one session was completed within 5 hours.

partially contain responses to the exposed orientation.

Animals were anesthetized as in surgery and paralyzed with pancuronium bromide (0.1 mg/kg/h). They were artificially ventilated. Contact lenses with appropriate curvatures were used to prevent the drying of eyes. The cortex was illuminated with a 700-nm wavelength light. The focal plane was adjusted to 500 μm below the cortical surface using a tandem-lens macroscope arrangement (Ratzlaff & Grinvald, 1991). Intrinsic optical signals were measured while the animals were exposed to visual stimuli displayed on a 20-inch CRT monitor placed 30 cm in front of the animal. Images were obtained with a CCD video camera, and digitized and stored in a computer. For each stimulus presentation, the intrinsic signal was recorded for 1.0 s before and 5.0 s after the stimulus onset. A blank stimulus was presented for 15 s between successive captures of intrinsic signals. Each visual stimulus was presented once in a pseudorandom sequence in a single trial of recordings. Twenty-six to 30 trials were collected in each recording session. As visual stimuli, we used full-screen squarewave gratings, which drifted in two directions at six equally spaced orientations (interval, 30°). To functionally identify area 17, we used gratings of a spatial frequency of 0.5 c.p.d., which is optimal for area 17 neurons (Bonhoeffer et al., 1995; Movshon et al. 1978; Ohki et al., 2000). The temporal frequency of the gratings was fixed at 2.0 Hz. The optical imaging in

The analysis methods that we used were described in a previous paper (Tanaka et al., 2006). It is noteworthy to explain the methods in some detail here to show that observed map changes are attributable to biological changes rather than artificial changes originating from our analysis methods. One trial of optical imaging was composed of six frames (duration of each frame, 1 s). To extract stimulus-related intrinsic signals, we subtracted signals recorded in the first frame (without stimulus presentation) from those signals recorded in succeeding frames with stimulus presentations. Then, we averaged the subtracted signals over the 4th to 6th frames for each trial. Next, we applied the generalized indicator function method to these averaged signals (Yokoo et al., 2001), which efficiently excluded noisy signals originating from volume and oxygenation changes in thick blood vessels and spatially slowly varying fluctuations of signals inherent in the recorded intrinsic signals. It should be noted that the image data processing based on the generalized indicator function method underestimates the effects of overrepresentation of exposed orientation induced by singleorientation exposure, because the data processing method eliminates spatially slowly varying point-spread components of intrinsic signal (Gilbert et al., 1996), which may

## **3. Orientation map plasticity in early life**

To investigate the visual cortical plasticity of orientation selectivity, we need to manipulate visual experience to expose animals to restricted orientations continuously and stably. However, manipulating experienced orientations is more difficult than depriving one eye by eyelid suture for the investigation of ocular dominance plasticity (Wiesel & Hubel, 1963). For the stable orientation-restricted visual experience, we fabricated simple-structured goggles fitted with cylindrical lenses, which can be easily attached and dettached to the forhead of cats. In addition, to avoid a sampling bias problem often encountered in unit recording, we employed optical imaging of intrinsic signal to measure orientation selectivity from a wide cortical area. Some kittens were reared with head-mounted goggles to experience a single orientation in a freely moving condition inside animal cages with their mother cats and littermates for two weeks. Some other kittens were reared without goggles, while the other conditions were the same as in goggle-reared kittens. These kittens were used for a control group.

## **3.1. Experimental methods**

The goggles were composed of planoconvex acrylic cylindrical lenses as shown in Fig. 6a (lens thickness, 10.0 mm; lens aperture diameter, 15.0 mm; lens power, +67 D). Cats on which goggles were mounted (Fig. 6b) were able to see elongated images of their environments through the goggles (Tanaka et al., 2007). For example, an image of concentric pattern is transformed to an image of vertical stripe (Figs. 6c and d). We used two types of goggles: vand h-goggles, which elongated visual images vertically and horizontally, respectively.

**Figure 6.** Cylindrical-lens-fitted goggles and visual image transformation.

Surgery was conducted according the procedure described in our previous papers (Tanaka et al., 2004, 2006). Initial anesthesia was induced using ketamine hydrochloride (5.0 mg/kg, i.m.) following sedation with medetomidine hydrochloride (0.1 mg/kg, i.m.). The animals were fixed on a stereotaxic apparatus and were artificially ventilated with a 60:40% mixture of N2O and O2 containing 0.5-1.0% isoflurane. Heart rate, end-tidal CO2 concentration, and rectal temperature were continuously monitored during surgery. A metal head holder for fixing the goggles and a metal chamber for optical imaging were cemented on the animal's skull using dental resin, and the skull and dura mater covering the recording area of the lateral gyrus were removed. The cranial window (17 mm × 12 mm) was positioned approximately from P5 to A12, spanning the midline. Next, the chamber was filled with 2% agar and sealed with a polyvinylidene chloride thin film and a plastic plate. Finally, the frame of the goggles was fixed to a head holder and the position of the goggles was calibrated so that the cylindrical lenses covered the visual field as widely as possible.

356 Visual Cortex – Current Status and Perspectives

used for a control group.

**3.1. Experimental methods** 

particularly around the crowns of gyri and perhaps at fundi.

**3. Orientation map plasticity in early life** 

superficial layers and deep layers in area 17. Moreover, the present model predicts that the preferred direction can more frequently reverse in the deeper layers around the gyrus than in the flat cortex, as reported by Berman et al. (1987). To date, it is thought that the cortical information representation obeys a columnar organization rule. The present study suggests that careful examination of functional architecture is needed, because different pieces of information can be represented in an identical anatomical minicolumn at different layers,

To investigate the visual cortical plasticity of orientation selectivity, we need to manipulate visual experience to expose animals to restricted orientations continuously and stably. However, manipulating experienced orientations is more difficult than depriving one eye by eyelid suture for the investigation of ocular dominance plasticity (Wiesel & Hubel, 1963). For the stable orientation-restricted visual experience, we fabricated simple-structured goggles fitted with cylindrical lenses, which can be easily attached and dettached to the forhead of cats. In addition, to avoid a sampling bias problem often encountered in unit recording, we employed optical imaging of intrinsic signal to measure orientation selectivity from a wide cortical area. Some kittens were reared with head-mounted goggles to experience a single orientation in a freely moving condition inside animal cages with their mother cats and littermates for two weeks. Some other kittens were reared without goggles, while the other conditions were the same as in goggle-reared kittens. These kittens were

The goggles were composed of planoconvex acrylic cylindrical lenses as shown in Fig. 6a (lens thickness, 10.0 mm; lens aperture diameter, 15.0 mm; lens power, +67 D). Cats on which goggles were mounted (Fig. 6b) were able to see elongated images of their environments through the goggles (Tanaka et al., 2007). For example, an image of concentric pattern is transformed to an image of vertical stripe (Figs. 6c and d). We used two types of goggles: vand h-goggles, which elongated visual images vertically and horizontally, respectively.

Surgery was conducted according the procedure described in our previous papers (Tanaka et al., 2004, 2006). Initial anesthesia was induced using ketamine hydrochloride (5.0 mg/kg, i.m.) following sedation with medetomidine hydrochloride (0.1 mg/kg, i.m.). The animals

**Figure 6.** Cylindrical-lens-fitted goggles and visual image transformation.

Animals were anesthetized as in surgery and paralyzed with pancuronium bromide (0.1 mg/kg/h). They were artificially ventilated. Contact lenses with appropriate curvatures were used to prevent the drying of eyes. The cortex was illuminated with a 700-nm wavelength light. The focal plane was adjusted to 500 μm below the cortical surface using a tandem-lens macroscope arrangement (Ratzlaff & Grinvald, 1991). Intrinsic optical signals were measured while the animals were exposed to visual stimuli displayed on a 20-inch CRT monitor placed 30 cm in front of the animal. Images were obtained with a CCD video camera, and digitized and stored in a computer. For each stimulus presentation, the intrinsic signal was recorded for 1.0 s before and 5.0 s after the stimulus onset. A blank stimulus was presented for 15 s between successive captures of intrinsic signals. Each visual stimulus was presented once in a pseudorandom sequence in a single trial of recordings. Twenty-six to 30 trials were collected in each recording session. As visual stimuli, we used full-screen squarewave gratings, which drifted in two directions at six equally spaced orientations (interval, 30°). To functionally identify area 17, we used gratings of a spatial frequency of 0.5 c.p.d., which is optimal for area 17 neurons (Bonhoeffer et al., 1995; Movshon et al. 1978; Ohki et al., 2000). The temporal frequency of the gratings was fixed at 2.0 Hz. The optical imaging in one session was completed within 5 hours.

The analysis methods that we used were described in a previous paper (Tanaka et al., 2006). It is noteworthy to explain the methods in some detail here to show that observed map changes are attributable to biological changes rather than artificial changes originating from our analysis methods. One trial of optical imaging was composed of six frames (duration of each frame, 1 s). To extract stimulus-related intrinsic signals, we subtracted signals recorded in the first frame (without stimulus presentation) from those signals recorded in succeeding frames with stimulus presentations. Then, we averaged the subtracted signals over the 4th to 6th frames for each trial. Next, we applied the generalized indicator function method to these averaged signals (Yokoo et al., 2001), which efficiently excluded noisy signals originating from volume and oxygenation changes in thick blood vessels and spatially slowly varying fluctuations of signals inherent in the recorded intrinsic signals. It should be noted that the image data processing based on the generalized indicator function method underestimates the effects of overrepresentation of exposed orientation induced by singleorientation exposure, because the data processing method eliminates spatially slowly varying point-spread components of intrinsic signal (Gilbert et al., 1996), which may partially contain responses to the exposed orientation.

Having excluded the spatially slow noise components, we summed the stimulus-related signals over all trials for each stimulus orientation and applied Gaussian low-pass filtering with a 150-μm standard deviation to eliminate high-frequency noise. In this way, we constructed a single-condition map for each stimulus orientation. To determine the preferred orientation at each pixel inside the recorded area, we used the vector sum method (Bonhoeffer & Grinvald, 1991). Thus, at each pixel, we obtained the preferred orientation and the modulation amplitude in the second harmonic components, which is regarded as orientation selectivity. The orientation polar map was constructed with the preferred orientation and orientation selectivity as color and brightness, respectively.

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 359

orientation, either vertical or horizontal, diminished drastically (Figs. 7e, f, k and l). A close examination of Figs. 7e and f revealed that the effect of vertical GR was reversed to a slight

**Figure 7.** Orientation selectivity in kittens continuously exposed to vertical- or horizontal orientation

Figure 8 illustrates the time profile of the sensitivity for the modification of orientation selectivity against the onset day of GR, where all data points were obtained from 18 kittens goggle-reared for 2 weeks. Here, we quantified the sensitivity as the relative size of the cortical domains for the exposed orientation, in which the average representation bias in normal kittens was subtracted at respective days of optical imaging performance (for more information, see Tanaka et al. 2007). Therefore, the positive and negative normalized relative domain size indicate the overrepresentation and underrepresentation of the exposed orientation, respectively. The sensitivity profiles for vertical- and horizontal-orientation exposures are generally consistent with each other, except for a discrepancy at 2-3 postnatal weeks. This discrepancy suggests that innate mechanisms of orientation map formation not only induce the representation bias toward horizontal orientation in normal kittens but also enhance the overrepresentation of horizontal orientation in young kittens exposed to horizontal orientation. The critical period can be defined as the postnatal period during which 2-week GR effectively causes the modification of orientation selectivity. Thus defined, the critical period starts 2 weeks after birth and lasts for 6 weeks. Note that the presently delineated critical period for orientation plasticity overlaps the most sensitive period for ocular dominance plasticity (postnatal 4-5 weeks) reported by Olson & Freeman (1980).

underrepresentation of the vertical orientation.

**3.3. Critical period for orientation selectivity** 

for 2 weeks.

For further analysis, we discarded pixels eliciting response strengths lower than a half of the response strength averaged over all pixels inside the recorded area. According to this criterion, the domains containing the remaining pixels nearly lined up with functionally defined area 17, which was exclusively activated by stimuli of a 0.5-c.p.d. spatial frequency. To construct an orientation histogram, we counted the number of pixels involved in each orientation, 30° width, and normalized them by the total number of pixels involved in all orientations.

### **3.2. Orientation map alteration by goggle rearing**

Before examining orientation maps in goggle-reared cats, we investigated orientation maps in normally reared cats. In orientation polar maps obtained from normally reared kittens younger than P30, generally the relative size of responsive domains is largest for horizontal orientation (0° or equivalently 180°) and smallest for vertical orientation (90°). This indicates that orientation representation is biased toward the horizontal orientation for very young normal kittens. This is analogous to the innate bias toward the contralateral eye, as has been known in ocular dominance (Wiesel & Hubel, 1963). However, the sample-averaged orientation histogram across 15 normally reared kittens of P33-84 showed a weak bias toward vertical orientation.

Here, we first describe the results of goggle rearing (GR) for two weeks. Measurements in goggle-reared kittens were completed within 5 hrs after the removal of the goggles. Figure 7 shows orientation polar maps (a, c, e, g, i and k) and orientation histograms (b, d, f, h, j and l) obtained from optical imaging of functionally defined area 17 (delineated by white curves) of those kittens. In orientation polar maps, color and brightness indicate preferred orientation and orientation selectivity, respectively (scale bars: 2 mm), and in orientation histograms, the height of bins indicates the relative size of cortical domains for 6 preferred orientations, where blue horizontal lines indicate the relative size of iso-orientation domains for a uniform orientation representation. Immediately after the 2-week GR that started at P16-P17, the overrepresentation of horizontal orientation was exclusive (Figs. 7g and h), whereas that of vertical orientation was somewhat moderate (Figs. 7a and b). For GR that started at P27-P29, the overrepresentation of vertical orientation also became nearly exclusive (Figs. 7c and d). The representation of horizontal orientation was still predominant, although that of unexposed orientations appeared (Figs. 7i and j). The imbalance between vertical and horizontal orientations at P16-P17 may reflect, at least partly, the horizontal bias detected in normal kittens. When 2-week GR was started at P49-54, the induced overrepresentation of the exposed orientation, either vertical or horizontal, diminished drastically (Figs. 7e, f, k and l). A close examination of Figs. 7e and f revealed that the effect of vertical GR was reversed to a slight underrepresentation of the vertical orientation.

**Figure 7.** Orientation selectivity in kittens continuously exposed to vertical- or horizontal orientation for 2 weeks.

### **3.3. Critical period for orientation selectivity**

358 Visual Cortex – Current Status and Perspectives

Having excluded the spatially slow noise components, we summed the stimulus-related signals over all trials for each stimulus orientation and applied Gaussian low-pass filtering with a 150-μm standard deviation to eliminate high-frequency noise. In this way, we constructed a single-condition map for each stimulus orientation. To determine the preferred orientation at each pixel inside the recorded area, we used the vector sum method (Bonhoeffer & Grinvald, 1991). Thus, at each pixel, we obtained the preferred orientation and the modulation amplitude in the second harmonic components, which is regarded as orientation selectivity. The orientation polar map was constructed with the preferred

For further analysis, we discarded pixels eliciting response strengths lower than a half of the response strength averaged over all pixels inside the recorded area. According to this criterion, the domains containing the remaining pixels nearly lined up with functionally defined area 17, which was exclusively activated by stimuli of a 0.5-c.p.d. spatial frequency. To construct an orientation histogram, we counted the number of pixels involved in each orientation, 30°

Before examining orientation maps in goggle-reared cats, we investigated orientation maps in normally reared cats. In orientation polar maps obtained from normally reared kittens younger than P30, generally the relative size of responsive domains is largest for horizontal orientation (0° or equivalently 180°) and smallest for vertical orientation (90°). This indicates that orientation representation is biased toward the horizontal orientation for very young normal kittens. This is analogous to the innate bias toward the contralateral eye, as has been known in ocular dominance (Wiesel & Hubel, 1963). However, the sample-averaged orientation histogram across 15 normally reared kittens of P33-84 showed a weak bias

Here, we first describe the results of goggle rearing (GR) for two weeks. Measurements in goggle-reared kittens were completed within 5 hrs after the removal of the goggles. Figure 7 shows orientation polar maps (a, c, e, g, i and k) and orientation histograms (b, d, f, h, j and l) obtained from optical imaging of functionally defined area 17 (delineated by white curves) of those kittens. In orientation polar maps, color and brightness indicate preferred orientation and orientation selectivity, respectively (scale bars: 2 mm), and in orientation histograms, the height of bins indicates the relative size of cortical domains for 6 preferred orientations, where blue horizontal lines indicate the relative size of iso-orientation domains for a uniform orientation representation. Immediately after the 2-week GR that started at P16-P17, the overrepresentation of horizontal orientation was exclusive (Figs. 7g and h), whereas that of vertical orientation was somewhat moderate (Figs. 7a and b). For GR that started at P27-P29, the overrepresentation of vertical orientation also became nearly exclusive (Figs. 7c and d). The representation of horizontal orientation was still predominant, although that of unexposed orientations appeared (Figs. 7i and j). The imbalance between vertical and horizontal orientations at P16-P17 may reflect, at least partly, the horizontal bias detected in normal kittens. When 2-week GR was started at P49-54, the induced overrepresentation of the exposed

width, and normalized them by the total number of pixels involved in all orientations.

orientation and orientation selectivity as color and brightness, respectively.

**3.2. Orientation map alteration by goggle rearing** 

toward vertical orientation.

Figure 8 illustrates the time profile of the sensitivity for the modification of orientation selectivity against the onset day of GR, where all data points were obtained from 18 kittens goggle-reared for 2 weeks. Here, we quantified the sensitivity as the relative size of the cortical domains for the exposed orientation, in which the average representation bias in normal kittens was subtracted at respective days of optical imaging performance (for more information, see Tanaka et al. 2007). Therefore, the positive and negative normalized relative domain size indicate the overrepresentation and underrepresentation of the exposed orientation, respectively. The sensitivity profiles for vertical- and horizontal-orientation exposures are generally consistent with each other, except for a discrepancy at 2-3 postnatal weeks. This discrepancy suggests that innate mechanisms of orientation map formation not only induce the representation bias toward horizontal orientation in normal kittens but also enhance the overrepresentation of horizontal orientation in young kittens exposed to horizontal orientation. The critical period can be defined as the postnatal period during which 2-week GR effectively causes the modification of orientation selectivity. Thus defined, the critical period starts 2 weeks after birth and lasts for 6 weeks. Note that the presently delineated critical period for orientation plasticity overlaps the most sensitive period for ocular dominance plasticity (postnatal 4-5 weeks) reported by Olson & Freeman (1980).

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 361

**Figure 9.** Effects of duration and timing of GR.

**3.5. Comparisons with previous studies** 

follows:

maps.

In 4 other kittens, in which long-term vertical GR for 4-6 weeks started at P21-P25 (triangles), the overrepresentation of the exposed orientation was retained at moderate levels between 0.39 and 0.64 of the normalized relative domain size in the first optical imaging. As tested in two of these kittens (P24-P73; P25-P74), the overrepresentation was preserved even after 3 weeks of normal viewing. A similar tendency was observed in another kitten in which horizontal GR started at P23 and switched to normal rearing at P51. We can summarize normal viewing effects on GR-induced changes of orientation maps as

1. When 2-week GR covers a relatively late phase of the critical period (at P29-39), normal viewing quickly eliminates the overrepresentation and restores normal orientation

2. When 2-week GR covers a relatively early phase of the critical period (earlier than P29),

3. Goggle rearing for 4 weeks or more outlasting the critical period acts to preserve the moderate overrepresentation of the exposed orientation, and reorganized orientation

There have been two hypotheses about the effect of visual experience on orientation plasticity. One is the selection hypothesis and the other the instruction hypothesis. In the

normal viewing does not completely eliminate the overrepresentation.

maps are consolidated to be robust against normal viewing thereafter.

**Figure 8.** Sensitivity profiles for orientation plasticity.

Figure 8 also shows that the critical period is followed by a late phase of underrepresentation of the exposed orientation for GR starting between P55 and P151. We also observed the case of an adult cat reared with vertical goggles from P396 for one month; this cat showed a small relative size of cortical domains representing the exposed orientation for 90o. Therefore, continuous single-orientation exposure after the critical period, even in adulthood, leads to the underrepresentation of the exposed orientation, as consistent with previous reports on orientation plasticity in adult cats (Creutzfeldt & Heggelund, 1975; Dragoi et al., 2000).

#### **3.4. Orientation map alteration under short- and long-term GR**

Figure 9 shows how the once-induced overrepresentation of the exposed orientation changes afterwards. The normalized relative sizes of cortical domains representing exposed vertical (circles) or horizontal (squares) orientations are plotted at the end of 2-week GR (solid symbols) and also at the end of the succeeding normal viewing (hollow symbols), respectively. Plotted points for identical kittens are linked by the lines, and numerical figures indicate onset ages of GR. In 4 kittens for which 2-week GR started at P29, 32, 37 and 39, 3-day normal viewing immediately before the second optical imaging eliminated the overrepresentation: the normalized relative domain sizes returned to 0 (level of normal kittens). However, in 4 kittens in which 2-week GR started relatively earlier at P16, P21 and P25, recovery during the succeeding 3-days of normal viewing was partial.

**Figure 9.** Effects of duration and timing of GR.

**Figure 8.** Sensitivity profiles for orientation plasticity.

Heggelund, 1975; Dragoi et al., 2000).

Figure 8 also shows that the critical period is followed by a late phase of underrepresentation of the exposed orientation for GR starting between P55 and P151. We also observed the case of an adult cat reared with vertical goggles from P396 for one month; this cat showed a small relative size of cortical domains representing the exposed orientation for 90o. Therefore, continuous single-orientation exposure after the critical period, even in adulthood, leads to the underrepresentation of the exposed orientation, as consistent with previous reports on orientation plasticity in adult cats (Creutzfeldt &

Figure 9 shows how the once-induced overrepresentation of the exposed orientation changes afterwards. The normalized relative sizes of cortical domains representing exposed vertical (circles) or horizontal (squares) orientations are plotted at the end of 2-week GR (solid symbols) and also at the end of the succeeding normal viewing (hollow symbols), respectively. Plotted points for identical kittens are linked by the lines, and numerical figures indicate onset ages of GR. In 4 kittens for which 2-week GR started at P29, 32, 37 and 39, 3-day normal viewing immediately before the second optical imaging eliminated the overrepresentation: the normalized relative domain sizes returned to 0 (level of normal kittens). However, in 4 kittens in which 2-week GR started relatively earlier at P16, P21 and

**3.4. Orientation map alteration under short- and long-term GR** 

P25, recovery during the succeeding 3-days of normal viewing was partial.

In 4 other kittens, in which long-term vertical GR for 4-6 weeks started at P21-P25 (triangles), the overrepresentation of the exposed orientation was retained at moderate levels between 0.39 and 0.64 of the normalized relative domain size in the first optical imaging. As tested in two of these kittens (P24-P73; P25-P74), the overrepresentation was preserved even after 3 weeks of normal viewing. A similar tendency was observed in another kitten in which horizontal GR started at P23 and switched to normal rearing at P51.

We can summarize normal viewing effects on GR-induced changes of orientation maps as follows:


#### **3.5. Comparisons with previous studies**

There have been two hypotheses about the effect of visual experience on orientation plasticity. One is the selection hypothesis and the other the instruction hypothesis. In the former hypothesis, after single-orientation exposure, neurons innately selective for unexposed orientations just decrease their responses to the unexposed orientations without changes of preferred orientations. In the latter hypothesis, neurons selective for unexposed orientations change their preferred orientations towards the exposed orientation. Blakemore & Cooper (1970) supported the instruction hypothesis, because they found preferred orientations of recorded units were strongly biased toward the exposed orientation in cats that had experienced striped environment. Later, Stryker et al. (1978) found that singleorientation exposure changed a large portion of units nonselective or unresponsive, although a proportion of responsive units preferring for the exposed orientation increased. Particularly, they found an orderly arrangement of selective units according to preferred orientation along the electrode tracks, as observed in normal cats, but clusters of nonselective or unresponsive units were interleaved. In our optical imaging on kittens exposed to a single orientation, stimulus-related intrinsic signals in response to unexposed orientations were reduced in cortical domains originally selective for the unexposed orientations, and a proportion of pixels without orientation selectivity tended to increase (Tanaka et al., 2006), consistently with single-unit recording by Stryker et al. (1978). However, stimulus-related intrinsic signals in response to the exposed orientation tended to increase in these domains, resulting in the changes of orientation preference.

New Pictures of the Structure and Plasticity of Orientation Columns in the Visual Cortex 363

exposure to stationary oriented stimuli is suggested to have a weak impact on the structural

The different effects of dynamic and stationary stripe exposures on orientation map alteration may be attributed to the differences in the behavioral significance of visual images through the goggles. Images transmitted through cylindrical leses contain some information on the animal's environment, in that vertically elongated stripes move associated with the movement of visual objects in the environment. It is, therefore, expected that the animal may pay attention to the images and neurons in the primary visual cortex can be sufficiently activated. Such neuronal activation consequently may have an influence on the suceptivility for orientation plasticity. In contrast, stripes exposed to by opaque lenses do not move irrespect of any dynamic changes of the environment. The animal may neglect the stripes and neurons in the primary visual cortex may not be sufficiently activated, resulting in weak or no induction of orientation plasticity. How the behavioral significance of exposed visual

Hubel & Wiesl's hypercolumn model successfully provides a simple view of the functional architecture of the cerebral cortex. However, our simulation of visual feature representation together with the high-resolution fMRI experiment on the cat threw a doubt about the idea of orientation columns extending from pia to white matter, especially in regions where the cortex is curved. Futhermore, the dimensionality of singularities in the orientation and direction maps was inceased by 1, when we considered the 3D visual cortex: Pinwheel centers in the orientation map appeared as point terminals of line singularities running in the 3D visual cortex at the cortical surface; direction-discontinuity lines starting or ending at pinwheel centers appeared as line crossings of direction-discontinuity sheets with the cortical surface. More extensive experiments are desired to confirm these observations.

In regrad to orientation plasticity in the developing visual cortex, it seems to be accepted that preferred orientations of neurons do not change but only responsiveness changes depending on visual experience. Our optical imaging experiments on cats, however, demonstrated that short-term single-orientation exposure dramatically altered preferred orientations until postnatal 6 weeks, which is against the current consensus. This study, on the other hand, revealed further complexity in orientation plasticity in case where animals were exposed to a single orientation for a long time or returned to a normal visual environment after single-orientation exposure. Again on this issue, further investigation is needed to obtain a better understanding of mechanisms underlying orientation plasticity in

images affects orientation plascity is an interesting future research target.

modification of orientation maps.

**4. Conclusion** 

the visual cortex.

**Author details** 

*The University of Electro-Communications, Japan* 

Shigeru Tanaka

Differences of experienced patterns during single-orientation exposure may be worth noting. Hirsch & Spinelli (1970) and Stryker et al. (1978) exposed kittens to stationary lines through their goggles. Carlson et al. (1986) also presented stationary stripe patterns with various spatial frequencies to monocularly deprived infant monkeys. To examine the effect of exposure to stationary oriented stimuli, we have reared 4 kittens with spherical-lens-fitted goggles for chronic exposure to a stationary stripe with a spatial frequency of about 0.5 and 0.15 c.p.d (Tanaka et al., 2007). Although the exposed orientation was overrepresented at the first optical imaging experiments after 2- or 3-week GR in 3 kittens, the underrepresentation of the exposed orientation occurred in the other kitten, in which the orthogonal orientation was overrepresented (data not shown). Even in the kittens showing the overrepresentation of the exposed orientation, the layouts of orientation preferences were labile during prolonged GR. In 3 of the 4 kittens, the overrepresentation disappeared or changed to the underrepresentation after long-term GR. Such labile alteration of orientation maps is characteristic of exposure to a stationary oriented pattern. This is contrasted with the finding that orientation maps altered by exposure to a dynamic single orientation through cylindrical-lens-fitted goggles are consolidated preserving the moderate overrepresentation of the exposed orientation (Fig. 9). It should be noted that the instability in orientation map alteration for stationary stripe pattern exposure was not due to the repeated optical imaging, because orientation maps altered by rearing with cylindrical-lens-fitted goggles changed gradually in successive optical imaging, and were finally stabilized at the moderate overrepresentation of the exposed orientation. The fact that Carlson et al. (1986) recorded units selective for the orthogonal orientation to the exposed orientation in the open eye may be such labile modification of orientation selectivity induced by the stationary stripe pattern exposure. The disappearance of the orientation selectivity modification for prolonged exposure to stationary oriented stimuli is suggested to have a weak impact on the structural modification of orientation maps.

The different effects of dynamic and stationary stripe exposures on orientation map alteration may be attributed to the differences in the behavioral significance of visual images through the goggles. Images transmitted through cylindrical leses contain some information on the animal's environment, in that vertically elongated stripes move associated with the movement of visual objects in the environment. It is, therefore, expected that the animal may pay attention to the images and neurons in the primary visual cortex can be sufficiently activated. Such neuronal activation consequently may have an influence on the suceptivility for orientation plasticity. In contrast, stripes exposed to by opaque lenses do not move irrespect of any dynamic changes of the environment. The animal may neglect the stripes and neurons in the primary visual cortex may not be sufficiently activated, resulting in weak or no induction of orientation plasticity. How the behavioral significance of exposed visual images affects orientation plascity is an interesting future research target.

## **4. Conclusion**

362 Visual Cortex – Current Status and Perspectives

former hypothesis, after single-orientation exposure, neurons innately selective for unexposed orientations just decrease their responses to the unexposed orientations without changes of preferred orientations. In the latter hypothesis, neurons selective for unexposed orientations change their preferred orientations towards the exposed orientation. Blakemore & Cooper (1970) supported the instruction hypothesis, because they found preferred orientations of recorded units were strongly biased toward the exposed orientation in cats that had experienced striped environment. Later, Stryker et al. (1978) found that singleorientation exposure changed a large portion of units nonselective or unresponsive, although a proportion of responsive units preferring for the exposed orientation increased. Particularly, they found an orderly arrangement of selective units according to preferred orientation along the electrode tracks, as observed in normal cats, but clusters of nonselective or unresponsive units were interleaved. In our optical imaging on kittens exposed to a single orientation, stimulus-related intrinsic signals in response to unexposed orientations were reduced in cortical domains originally selective for the unexposed orientations, and a proportion of pixels without orientation selectivity tended to increase (Tanaka et al., 2006), consistently with single-unit recording by Stryker et al. (1978). However, stimulus-related intrinsic signals in response to the exposed orientation tended to

increase in these domains, resulting in the changes of orientation preference.

Differences of experienced patterns during single-orientation exposure may be worth noting. Hirsch & Spinelli (1970) and Stryker et al. (1978) exposed kittens to stationary lines through their goggles. Carlson et al. (1986) also presented stationary stripe patterns with various spatial frequencies to monocularly deprived infant monkeys. To examine the effect of exposure to stationary oriented stimuli, we have reared 4 kittens with spherical-lens-fitted goggles for chronic exposure to a stationary stripe with a spatial frequency of about 0.5 and 0.15 c.p.d (Tanaka et al., 2007). Although the exposed orientation was overrepresented at the first optical imaging experiments after 2- or 3-week GR in 3 kittens, the underrepresentation of the exposed orientation occurred in the other kitten, in which the orthogonal orientation was overrepresented (data not shown). Even in the kittens showing the overrepresentation of the exposed orientation, the layouts of orientation preferences were labile during prolonged GR. In 3 of the 4 kittens, the overrepresentation disappeared or changed to the underrepresentation after long-term GR. Such labile alteration of orientation maps is characteristic of exposure to a stationary oriented pattern. This is contrasted with the finding that orientation maps altered by exposure to a dynamic single orientation through cylindrical-lens-fitted goggles are consolidated preserving the moderate overrepresentation of the exposed orientation (Fig. 9). It should be noted that the instability in orientation map alteration for stationary stripe pattern exposure was not due to the repeated optical imaging, because orientation maps altered by rearing with cylindrical-lens-fitted goggles changed gradually in successive optical imaging, and were finally stabilized at the moderate overrepresentation of the exposed orientation. The fact that Carlson et al. (1986) recorded units selective for the orthogonal orientation to the exposed orientation in the open eye may be such labile modification of orientation selectivity induced by the stationary stripe pattern exposure. The disappearance of the orientation selectivity modification for prolonged Hubel & Wiesl's hypercolumn model successfully provides a simple view of the functional architecture of the cerebral cortex. However, our simulation of visual feature representation together with the high-resolution fMRI experiment on the cat threw a doubt about the idea of orientation columns extending from pia to white matter, especially in regions where the cortex is curved. Futhermore, the dimensionality of singularities in the orientation and direction maps was inceased by 1, when we considered the 3D visual cortex: Pinwheel centers in the orientation map appeared as point terminals of line singularities running in the 3D visual cortex at the cortical surface; direction-discontinuity lines starting or ending at pinwheel centers appeared as line crossings of direction-discontinuity sheets with the cortical surface. More extensive experiments are desired to confirm these observations.

In regrad to orientation plasticity in the developing visual cortex, it seems to be accepted that preferred orientations of neurons do not change but only responsiveness changes depending on visual experience. Our optical imaging experiments on cats, however, demonstrated that short-term single-orientation exposure dramatically altered preferred orientations until postnatal 6 weeks, which is against the current consensus. This study, on the other hand, revealed further complexity in orientation plasticity in case where animals were exposed to a single orientation for a long time or returned to a normal visual environment after single-orientation exposure. Again on this issue, further investigation is needed to obtain a better understanding of mechanisms underlying orientation plasticity in the visual cortex.

## **Author details**

Shigeru Tanaka *The University of Electro-Communications, Japan* 

#### **5. References**

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**Chapter 17** 

© 2012 Martínez-Millán et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**The Experimental Manipulation** 

I. Gerrikagoitia, B. Rienda and L. Martínez-Millán

The visual cortex is part of the occipital cortex that makes up the primary and secondary visual areas [1,2]. In the primary visual areas of rodents, as in other isocortical areas, two main neuronal types are present: inhibitory interneurons and projecting neurons [3,4]. The inhibitory interneurons belong to several GABAergic subpopulations, while the projection neurons are excitatory pyramidal neurons that are classically distributed in 5 layers, each of which is associated with a preferential projection area [3,4]. Accordingly, the pyramidal neurons of layers II, III and IV give rise to corticocortical connections, while those of layers V and VI project to subcortical structures. In the primary visual cortex pyramidal neurons of layer V project to superficial collicular layers and they give rise to collaterals that project to

The aim of this review is to describe the sprouting capacities of these projecting neurons and to evaluate several strategies to enhance these capabilities in adult animals, principally considering work carried out in rodents. In the first part, we will discuss the sprouting of the corticocollicular ipsilateral connection in young animals. This connection originates in layer V pyramidal neurons and its post-lesional sprouting capacities diminish significantly after the end of the critical period (postnatal day 45). We will also discuss the use of siRNAs to knockdown the expression of molecules that inhibit post-lesional axonal sprouting in adults. Lastly, we will describe alterations in sprouting and synaptic size in the

**2. Differential lesion responses of neonatal and adult visual cortex** 

The visual system is widely used as a model to study plasticity, given the compartmentalized arrangement of its main stations. In rodents, most of the retinal ganglion

and reproduction in any medium, provided the original work is properly cited.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48632

**1. Introduction** 

the pontine nuclei.

**efferents** 

corticocortical visual connections.

**of Visual Cortex Efferents** 


## **The Experimental Manipulation of Visual Cortex Efferents**

I. Gerrikagoitia, B. Rienda and L. Martínez-Millán

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48632

## **1. Introduction**

366 Visual Cortex – Current Status and Perspectives

Vol. 30, pp. 462 – 477

206-214

experiment. *Neural Networks,* Vol. 17, pp. 1363–1375

Tanaka, S., Ribot, J., & Miyashita, M. (2004). Roles of visual experience and intrinsic mechanism in the activity-dependent self-organization of orientation maps: Theory and

Tanaka, S., Ribot, J., Imamura, K., & Tani, T. (2006). Orientation-restricted continuous visual exposure induces marked reorganization of orientation maps in early life. *NeuroImage,*

Tanaka, S., Tani, T., Ribot, J., & Yamazaki, T. (2007). Chronically mountable goggles for persistent exposure to single orientation. *Journal of Neuroscience Methods,* Vol. 160, pp.

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extraction from noisy multivariate data. *NeuroImage,* Vol. 14, pp. 1309-1326

The visual cortex is part of the occipital cortex that makes up the primary and secondary visual areas [1,2]. In the primary visual areas of rodents, as in other isocortical areas, two main neuronal types are present: inhibitory interneurons and projecting neurons [3,4]. The inhibitory interneurons belong to several GABAergic subpopulations, while the projection neurons are excitatory pyramidal neurons that are classically distributed in 5 layers, each of which is associated with a preferential projection area [3,4]. Accordingly, the pyramidal neurons of layers II, III and IV give rise to corticocortical connections, while those of layers V and VI project to subcortical structures. In the primary visual cortex pyramidal neurons of layer V project to superficial collicular layers and they give rise to collaterals that project to the pontine nuclei.

The aim of this review is to describe the sprouting capacities of these projecting neurons and to evaluate several strategies to enhance these capabilities in adult animals, principally considering work carried out in rodents. In the first part, we will discuss the sprouting of the corticocollicular ipsilateral connection in young animals. This connection originates in layer V pyramidal neurons and its post-lesional sprouting capacities diminish significantly after the end of the critical period (postnatal day 45). We will also discuss the use of siRNAs to knockdown the expression of molecules that inhibit post-lesional axonal sprouting in adults. Lastly, we will describe alterations in sprouting and synaptic size in the corticocortical visual connections.

## **2. Differential lesion responses of neonatal and adult visual cortex efferents**

The visual system is widely used as a model to study plasticity, given the compartmentalized arrangement of its main stations. In rodents, most of the retinal ganglion

© 2012 Martínez-Millán et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

axons cross the optic chiasm to the contralateral side [5,6]. Thus, retinal deafferentation is a convenient experimental means of investigating the plastic response mechanisms to central nervous system (CNS) lesions.

The Experimental Manipulation of Visual Cortex Efferents 369

may occur due to axonal sprouting and/or the blockade of developmentally regulated axonal retraction. It has been suggested that axons continuously compete for postsynaptic sites in the CNS. Indeed, it is likely that during development this competition is essential for the formation and refinement of projections, although an equilibrium is reached in the mature nervous system that results in the stabilization of neuronal connections [31,32].

In previous studies, we observed an enlargement of the visual corticocollicular terminal field in rabbits after neonatal removal of contralateral retinal inputs [33], and an alteration in the plastic response to injury when the same lesion was performed in adults [34]. The anterograde axonal tracer biotin dextran amine (BDA) was used to label the corticocollicular connection emerging from layer V pyramidal neurons of the primary visual cortex in 3 different experimental groups: (i) adult rats (P60) subjected to neonatal (P1) optic nerve transection; (ii) adults rats subjected to optic nerve transection in adulthood; and (iii) control adults rats. The animals were sacrificed 10 days after BDA injection and the superior colliculi extracted for histochemical analysis. As BDA was injected into the region of the primary visual cortex that represents the lower temporal visual field [35-36], corticocollicular terminal fields were localized within the posterolateral quadrant of the SC [37] in all experimental animals. In agreement with previous studies [14,33,38], we observed a tight topographical organization of the visual corticocollicular terminal field. In control animals, the corticocollicular terminal field was column-shaped, extending from the SO up to the pial surface, and it was restricted to a small portion of the collicular surface. Fibers ascending from the SO gave rise to dense axonal networks in the lower half of SGS, and they branched to reach the upper half of this stratum and the most superficial SZ, where the

Visual deprivation in neonatal animals results in significant expansion of the corticocollicular visual terminal fields, which invaded the entire lateromedial extension of the visual collicular strata. However, the axons tended to concentrate in the posterolateral quadrant of the collicular surface, indicating that the gross topography of the connection was maintained, despite deafferentation [34]. Molecules involved in target path finding, such as ephrins and their receptors, may play a crucial role in determining retinotectal topography [39-41]. Neonatal deafferentation also significantly alters the direction of fiber projection, resulting in horizontal and oblique orientation in the majority of fibers within the SGS in deafferented animals. We previously reported a similar effect in rabbits [33]. However, the expansion of this terminal field may reflect the maintenance of collaterals during postnatal development [38,42] or active sprouting processes. Previous studies reported that in neonatal animals, corticocollicular fibers only appear in the SO [38,43]. As we observed a large density of fibers occupying almost the entire extension of the most superficial strata, SZ and SO, we can assert that an active process of axonal pruning

The labeled visual corticocolicular terminal fields in rats subjected to retinal deafferentation in adulthood were columnar, with no changes in extension, although staining was most intense in the upper half of the SGS and in the SZ [34]. Anterograde labeling with BDA allowed clear morphological identification of presynaptic boutons, and quantification of

fibers were oriented parallel to the collicular surface [34].

occurred after neonatal deafferentation.

The superior colliculus (SC) is a layered mesencephalic structure that can be divided into two main compartments: the superficial strata that are mainly devoted to visual function; and the intermediate and deep strata that process multisensorial information [7-9]. The superficial layers are composed of the stratum zonale (SZ), stratum griseum superficiale (SGS), and stratum opticum (SO), and they receive their main afferent input from the retina and primary visual cortex.

In rats, virtually all retinal ganglion cells project to the contralateral SC [6,10] and the majority of optic axons reach the SC prenatally, with the remainder reaching their target in the early postnatal days [11]. Layer V pyramidal neurons of the primary visual cortex (VC1) project to the ipsilateral SC [12-14], with the first visual cortical axons that reach the SC arriving on postnatal day (P) 4/5. At this stage, the axons only appear in the SO. From P7 to P13, these projections spread out to the ventral region of the SGS and the intermediate layers, and between P13 and P19, connections are restricted to the superficial strata of the SC, ultimately forming the organizational pattern seen in adults [15]. Although both retinocollicular and corticocollicular terminals densely innervate superficial strata of the SC, the former ramify more densely in the SZ and upper SGS, while the latter project to the lower SGS and upper SO [12-14]. During development, retinal and primary visual cortex fibers undergo multiple plastic changes, which include axonal growth, target path finding, axonal pruning and projection refinement [15-17]. This results in the formation of a precisely organized topographic map that represents the visual field in the SC in a point-to-point fashion.

CNS lesions or pathologies, and the deprivation of visual stimuli, can alter the final visual corticocollicular organization, predisposing this system to phenomena of neuroplasticity [18]. The capacity to respond to CNS lesions through plastic changes varies depending on the age at which the injury takes place. Thus, during early postnatal development, while connections are being established, neuronal projections exhibit significant capacity for regeneration and reorganization in response to neuronal damage. However, this postlesional response becomes considerably diminished in adulthood. A remarkable number of publications have described changes in the organization of neuronal connections following neonatal CNS injury. In the visual system, retinal deafferentation at birth results in severe alterations of the afferent systems that project to SC superficial layers [19]. For example, removal of SC input in neonatal rodents results in an aberrant ipsilateral retinotectal projection [20-24], whereas retinal deafferentation in adults has no such effect [25-27]. Gradual, continuous plastic changes have been described in the ipsilateral retinal axons of adult rats subjected to contralateral retinal lesions at P21, in contrast to the fast plastic response observed in neonatal rats evident within 48 hours of lesion [28].

Neonatal lesions of the visual cortex give rise to an aberrant projection to the contralateral SC [29] and expansion of the ipsilateral corticocollicular projection from the remaining unlesioned visual cortex [30]. These plastic responses during early postnatal development may occur due to axonal sprouting and/or the blockade of developmentally regulated axonal retraction. It has been suggested that axons continuously compete for postsynaptic sites in the CNS. Indeed, it is likely that during development this competition is essential for the formation and refinement of projections, although an equilibrium is reached in the mature nervous system that results in the stabilization of neuronal connections [31,32].

368 Visual Cortex – Current Status and Perspectives

nervous system (CNS) lesions.

and primary visual cortex.

axons cross the optic chiasm to the contralateral side [5,6]. Thus, retinal deafferentation is a convenient experimental means of investigating the plastic response mechanisms to central

The superior colliculus (SC) is a layered mesencephalic structure that can be divided into two main compartments: the superficial strata that are mainly devoted to visual function; and the intermediate and deep strata that process multisensorial information [7-9]. The superficial layers are composed of the stratum zonale (SZ), stratum griseum superficiale (SGS), and stratum opticum (SO), and they receive their main afferent input from the retina

In rats, virtually all retinal ganglion cells project to the contralateral SC [6,10] and the majority of optic axons reach the SC prenatally, with the remainder reaching their target in the early postnatal days [11]. Layer V pyramidal neurons of the primary visual cortex (VC1) project to the ipsilateral SC [12-14], with the first visual cortical axons that reach the SC arriving on postnatal day (P) 4/5. At this stage, the axons only appear in the SO. From P7 to P13, these projections spread out to the ventral region of the SGS and the intermediate layers, and between P13 and P19, connections are restricted to the superficial strata of the SC, ultimately forming the organizational pattern seen in adults [15]. Although both retinocollicular and corticocollicular terminals densely innervate superficial strata of the SC, the former ramify more densely in the SZ and upper SGS, while the latter project to the lower SGS and upper SO [12-14]. During development, retinal and primary visual cortex fibers undergo multiple plastic changes, which include axonal growth, target path finding, axonal pruning and projection refinement [15-17]. This results in the formation of a precisely organized topographic map that

CNS lesions or pathologies, and the deprivation of visual stimuli, can alter the final visual corticocollicular organization, predisposing this system to phenomena of neuroplasticity [18]. The capacity to respond to CNS lesions through plastic changes varies depending on the age at which the injury takes place. Thus, during early postnatal development, while connections are being established, neuronal projections exhibit significant capacity for regeneration and reorganization in response to neuronal damage. However, this postlesional response becomes considerably diminished in adulthood. A remarkable number of publications have described changes in the organization of neuronal connections following neonatal CNS injury. In the visual system, retinal deafferentation at birth results in severe alterations of the afferent systems that project to SC superficial layers [19]. For example, removal of SC input in neonatal rodents results in an aberrant ipsilateral retinotectal projection [20-24], whereas retinal deafferentation in adults has no such effect [25-27]. Gradual, continuous plastic changes have been described in the ipsilateral retinal axons of adult rats subjected to contralateral retinal lesions at P21, in contrast to the fast plastic

Neonatal lesions of the visual cortex give rise to an aberrant projection to the contralateral SC [29] and expansion of the ipsilateral corticocollicular projection from the remaining unlesioned visual cortex [30]. These plastic responses during early postnatal development

represents the visual field in the SC in a point-to-point fashion.

response observed in neonatal rats evident within 48 hours of lesion [28].

In previous studies, we observed an enlargement of the visual corticocollicular terminal field in rabbits after neonatal removal of contralateral retinal inputs [33], and an alteration in the plastic response to injury when the same lesion was performed in adults [34]. The anterograde axonal tracer biotin dextran amine (BDA) was used to label the corticocollicular connection emerging from layer V pyramidal neurons of the primary visual cortex in 3 different experimental groups: (i) adult rats (P60) subjected to neonatal (P1) optic nerve transection; (ii) adults rats subjected to optic nerve transection in adulthood; and (iii) control adults rats. The animals were sacrificed 10 days after BDA injection and the superior colliculi extracted for histochemical analysis. As BDA was injected into the region of the primary visual cortex that represents the lower temporal visual field [35-36], corticocollicular terminal fields were localized within the posterolateral quadrant of the SC [37] in all experimental animals. In agreement with previous studies [14,33,38], we observed a tight topographical organization of the visual corticocollicular terminal field. In control animals, the corticocollicular terminal field was column-shaped, extending from the SO up to the pial surface, and it was restricted to a small portion of the collicular surface. Fibers ascending from the SO gave rise to dense axonal networks in the lower half of SGS, and they branched to reach the upper half of this stratum and the most superficial SZ, where the fibers were oriented parallel to the collicular surface [34].

Visual deprivation in neonatal animals results in significant expansion of the corticocollicular visual terminal fields, which invaded the entire lateromedial extension of the visual collicular strata. However, the axons tended to concentrate in the posterolateral quadrant of the collicular surface, indicating that the gross topography of the connection was maintained, despite deafferentation [34]. Molecules involved in target path finding, such as ephrins and their receptors, may play a crucial role in determining retinotectal topography [39-41]. Neonatal deafferentation also significantly alters the direction of fiber projection, resulting in horizontal and oblique orientation in the majority of fibers within the SGS in deafferented animals. We previously reported a similar effect in rabbits [33]. However, the expansion of this terminal field may reflect the maintenance of collaterals during postnatal development [38,42] or active sprouting processes. Previous studies reported that in neonatal animals, corticocollicular fibers only appear in the SO [38,43]. As we observed a large density of fibers occupying almost the entire extension of the most superficial strata, SZ and SO, we can assert that an active process of axonal pruning occurred after neonatal deafferentation.

The labeled visual corticocolicular terminal fields in rats subjected to retinal deafferentation in adulthood were columnar, with no changes in extension, although staining was most intense in the upper half of the SGS and in the SZ [34]. Anterograde labeling with BDA allowed clear morphological identification of presynaptic boutons, and quantification of boutons in the terminal fields revealed a maximal density in the SGS and the SO [34]. Similar results were obtained by counting autoradiographic particles following [3H]-leucine injection into the primary visual cortex [38]. Despite occurring in neonatal deafferented animals, the increase in bouton density in the absence of notable axonal arborization suggests that new synaptic terminals are formed and thus, we conclude that adult visual corticocollicular afferents maintain a certain degree of plasticity. Comparable synaptogenic responses in the adult corticorubral axons have been described following red nucleus deafferentation [44]. Cytoskeletal proteins like GAP-43 have been implicated in axonal growth [45], and GAP-43 expression in the visual cortex is abundant during postnatal development but it decreases in adulthood [46]. These observations may explain the differences in axonal branching between deafferented neonates and adults. Indeed, we also found that immature vimentin-expressing astrocytes are abundant in the neonatal SC [47], where they may induce local sprouting after retinal deafferentation.

The Experimental Manipulation of Visual Cortex Efferents 371

NgR, a GPI-linked protein with multiple leucine-rich repeats, is the receptor for Nogo-66, and it mediates the signaling cascade that inhibits axonal growth [68]. More recent studies have shown that MAG and OMgp can also bind to NgR to exert their inhibitory actions [69- 71]. The neurotrophin receptor p75 (p75NTR) forms a complex with NgR that mediates axonal growth inhibition and that initiates the signaling cascade triggered by myelin derived inhibitors [67,71,72]. p75NTR is not ubiquitously expressed in the adult brain, whereas almost all mature CNS neurons respond to inhibition by myelin. Thus, it is likely that other proteins assume the function of p75NTR. TROY is an orphan member of the tumor necrosis factor receptor (TNFR) superfamily that is widely expressed by both embryonic and adult neurons [73,74], and it has been identified as a functional homolog of p75NTR that may contribute to the inhibitory effects of myelin [75,76]. Nonetheless, the role of TROY as a signal transducing receptor in the inhibition of axonal growth remains unclear, as its expression has not been consistently demonstrated in the adult CNS [77]. Lingo-1, the third component of this receptor complex [78], belongs to a large family of proteins that contain leucine-rich repeats and immunoglobulins [79]. Physical association of Lingo-1, NgR and p75NTR results in the formation of a tripartite receptor complex that mediates the inhibitory signaling triggered by myelin inhibitors [78], and the intracellular signaling cascade this complex activates alters the Rac1/RhoA balance in growth cones. RhoA, Rac1 and Cdc42 are widely expressed members of the small GTPase family that regulate actin dynamics and microtubule assembly. Rac1 and RhoA exert antagonistic effects on growth cone dynamics via their effector-kinases, PAK1 and ROCK, stimulating growth cone motility and inducing collapse, respectively. In the damaged nervous system, myelin-derived inhibitors alter the Rac1 and RhoA signaling equilibrium, augmenting RhoA activity at the expense of Rac1 activity [80, 81]. RhoA activation activates the sequential ROCK/LIM kinase/cofilin signaling cascade, resulting in the depolymerization of actin filaments and subsequent growth cone collapse [82]. This intracellular mechanism can be influenced by several molecules, including MAG, Nogo, OMgp, Netrin-1, ephrins and CSPGs, and it has been proposed as the convergence point of several inhibitors of axonal growth that exert similar functions

**4. Strategies to promote corticocollicular sprouting after visual** 

Several strategies have been described to promote the regeneration and reorganization of neuronal connections following CNS injury. Regeneration of mature damaged axons has been demonstrated using antibodies against myelin-derived inhibitors. For example, treatment of adult rats with anti-Nogo-A IN-1 after spinal cord lesions promotes significant axonal sprouting and regeneration over long distances caudal to the lesion site, accompanied by motor improvements and restoration of sensorial function [86-88]. Similarly, in animal models of spinal cord injury and stroke, the intrathecal administration of antibodies that effectively neutralize Nogo-A activity enhances regeneration of the corticospinal tract fibers, restoring damaged neuronal circuits and promoting functional recovery [89,90]. In support of these findings, the intrathecal administration of anti-Nogo-A

[78,80,83-85].

**deafferentation in adulthood** 

In conclusion, our findings demonstrate that the capacity for post-lesional remodeling is partially retained by the adult central nervous system.

## **3. Molecular determinants involved in the dampening of the plastic response during adulthood**

There is evidence accumulating that glial scar-associated molecules and myelin-derived molecules are molecular determinants that contribute to the diminished ability of adult neurons to regenerate their axons and reorganize their connections following CNS lesions. The glial scar is a meshwork composed of reactive astrocytes, oligodendrocyte precursors, meningeal fibroblasts and microglia that migrate to the lesion site to mediate tightly linked processes. Not only is it an impenetrable physical barrier to regenerating axons but it is also an important source of molecules that directly inhibit regeneration. After neuronal injury, reactive astrocytes and meningeal fibroblasts in the glial scar rapidly enhance the production and release of extracellular matrix molecules, such as the chondroitin sulfate proteoglycans (CSPGs), which are important inhibitors of axonal growth [48-49]. In addition, molecules involved in axonal path finding, such as ephrins and ephrin A4 receptor [50,51], semaphorin 3A [52-54] and Slit proteins [55], have been implicated in the mechanisms by which the gliar scar prevents axonal growth [56].

Myelin also mediates the inhibition of axonal growth in the CNS and for 30 years, postlesional products of CNS myelin have been known to specifically inhibit axonal extension [57]. Subsequent studies confirmed that CNS myelin and mature oligodendrocytes contain molecular components that restrict axonal regeneration [58-61]. Several proteins expressed by oligodendrocytes have been identified as myelin-associated inhibitors on the basis of their ability to inhibit neurite outgrowth and induce growth cone collapse. Of these, Nogo [62-64], myelin associated glycoprotein (MAG) [65,66], and oligodendrocyte myelin glycoprotein (OMgp) [67] are considered the main contributors to the inhibitory effects of CNS myelin.

NgR, a GPI-linked protein with multiple leucine-rich repeats, is the receptor for Nogo-66, and it mediates the signaling cascade that inhibits axonal growth [68]. More recent studies have shown that MAG and OMgp can also bind to NgR to exert their inhibitory actions [69- 71]. The neurotrophin receptor p75 (p75NTR) forms a complex with NgR that mediates axonal growth inhibition and that initiates the signaling cascade triggered by myelin derived inhibitors [67,71,72]. p75NTR is not ubiquitously expressed in the adult brain, whereas almost all mature CNS neurons respond to inhibition by myelin. Thus, it is likely that other proteins assume the function of p75NTR. TROY is an orphan member of the tumor necrosis factor receptor (TNFR) superfamily that is widely expressed by both embryonic and adult neurons [73,74], and it has been identified as a functional homolog of p75NTR that may contribute to the inhibitory effects of myelin [75,76]. Nonetheless, the role of TROY as a signal transducing receptor in the inhibition of axonal growth remains unclear, as its expression has not been consistently demonstrated in the adult CNS [77]. Lingo-1, the third component of this receptor complex [78], belongs to a large family of proteins that contain leucine-rich repeats and immunoglobulins [79]. Physical association of Lingo-1, NgR and p75NTR results in the formation of a tripartite receptor complex that mediates the inhibitory signaling triggered by myelin inhibitors [78], and the intracellular signaling cascade this complex activates alters the Rac1/RhoA balance in growth cones. RhoA, Rac1 and Cdc42 are widely expressed members of the small GTPase family that regulate actin dynamics and microtubule assembly. Rac1 and RhoA exert antagonistic effects on growth cone dynamics via their effector-kinases, PAK1 and ROCK, stimulating growth cone motility and inducing collapse, respectively. In the damaged nervous system, myelin-derived inhibitors alter the Rac1 and RhoA signaling equilibrium, augmenting RhoA activity at the expense of Rac1 activity [80, 81]. RhoA activation activates the sequential ROCK/LIM kinase/cofilin signaling cascade, resulting in the depolymerization of actin filaments and subsequent growth cone collapse [82]. This intracellular mechanism can be influenced by several molecules, including MAG, Nogo, OMgp, Netrin-1, ephrins and CSPGs, and it has been proposed as the convergence point of several inhibitors of axonal growth that exert similar functions [78,80,83-85].

370 Visual Cortex – Current Status and Perspectives

boutons in the terminal fields revealed a maximal density in the SGS and the SO [34]. Similar results were obtained by counting autoradiographic particles following [3H]-leucine injection into the primary visual cortex [38]. Despite occurring in neonatal deafferented animals, the increase in bouton density in the absence of notable axonal arborization suggests that new synaptic terminals are formed and thus, we conclude that adult visual corticocollicular afferents maintain a certain degree of plasticity. Comparable synaptogenic responses in the adult corticorubral axons have been described following red nucleus deafferentation [44]. Cytoskeletal proteins like GAP-43 have been implicated in axonal growth [45], and GAP-43 expression in the visual cortex is abundant during postnatal development but it decreases in adulthood [46]. These observations may explain the differences in axonal branching between deafferented neonates and adults. Indeed, we also found that immature vimentin-expressing astrocytes are abundant in the neonatal SC [47],

In conclusion, our findings demonstrate that the capacity for post-lesional remodeling is

There is evidence accumulating that glial scar-associated molecules and myelin-derived molecules are molecular determinants that contribute to the diminished ability of adult neurons to regenerate their axons and reorganize their connections following CNS lesions. The glial scar is a meshwork composed of reactive astrocytes, oligodendrocyte precursors, meningeal fibroblasts and microglia that migrate to the lesion site to mediate tightly linked processes. Not only is it an impenetrable physical barrier to regenerating axons but it is also an important source of molecules that directly inhibit regeneration. After neuronal injury, reactive astrocytes and meningeal fibroblasts in the glial scar rapidly enhance the production and release of extracellular matrix molecules, such as the chondroitin sulfate proteoglycans (CSPGs), which are important inhibitors of axonal growth [48-49]. In addition, molecules involved in axonal path finding, such as ephrins and ephrin A4 receptor [50,51], semaphorin 3A [52-54] and Slit proteins [55], have been implicated in the

Myelin also mediates the inhibition of axonal growth in the CNS and for 30 years, postlesional products of CNS myelin have been known to specifically inhibit axonal extension [57]. Subsequent studies confirmed that CNS myelin and mature oligodendrocytes contain molecular components that restrict axonal regeneration [58-61]. Several proteins expressed by oligodendrocytes have been identified as myelin-associated inhibitors on the basis of their ability to inhibit neurite outgrowth and induce growth cone collapse. Of these, Nogo [62-64], myelin associated glycoprotein (MAG) [65,66], and oligodendrocyte myelin glycoprotein (OMgp) [67] are considered the main contributors to the inhibitory effects of

**3. Molecular determinants involved in the dampening of the plastic** 

where they may induce local sprouting after retinal deafferentation.

mechanisms by which the gliar scar prevents axonal growth [56].

partially retained by the adult central nervous system.

**response during adulthood** 

CNS myelin.

## **4. Strategies to promote corticocollicular sprouting after visual deafferentation in adulthood**

Several strategies have been described to promote the regeneration and reorganization of neuronal connections following CNS injury. Regeneration of mature damaged axons has been demonstrated using antibodies against myelin-derived inhibitors. For example, treatment of adult rats with anti-Nogo-A IN-1 after spinal cord lesions promotes significant axonal sprouting and regeneration over long distances caudal to the lesion site, accompanied by motor improvements and restoration of sensorial function [86-88]. Similarly, in animal models of spinal cord injury and stroke, the intrathecal administration of antibodies that effectively neutralize Nogo-A activity enhances regeneration of the corticospinal tract fibers, restoring damaged neuronal circuits and promoting functional recovery [89,90]. In support of these findings, the intrathecal administration of anti-Nogo-A

antibodies in monkeys subjected to cervical spinal cord hemisection promotes extensive functional recovery, increased sprouting and regenerative axonal elongation [91].

The Experimental Manipulation of Visual Cortex Efferents 373

histochemical analysis. Control rats received the same siRNA injections into the primary visual cortex. The effect of the siRNAs on NgR and RhoA mRNA levels were measured by

Microinjection of siRNAs against NgR and RhoA into the primary visual cortex of adult enucleated rats promoted a mild expansion of the ipsilateral visual corticocollicular terminal field, although in both cases the centre of the field presented a characteristic column-like shape extending from the SO up to the pial surface, a similar pattern to that seen in nonsiRNA treated animals. Likewise, following siRNA injection, many fibers were observed running parallel to the pial surface, mainly located within the ventral half of the SGS and running away from the terminal field center towards the middle line. Moreover, several growth cone-bearing axons were observed in these cases, suggesting active axonal growth

To confirm the inhibitory effect of siRNAs on NgR and RhoA mRNA expression in the primary visual cortex, and hence the involvement of these molecules in the reorganization of the visual corticocollicular field in adult rats subjected to retinal deafferentation, relative mRNA levels were quantified by qRT-PCR 24 hours after siRNA injection. This revealed significant decreases in NgR and RhoA mRNA levels (44.8 ± 7.3% and 21.67 ± 10.53%,

These results demonstrate that siRNA-mediated abolition of the expression of key mediators of axonal growth inhibition, such as NgR and more notably RhoA, promotes axonal outgrowth after adult CNS injury. Indeed, recent studies using different approaches to reduce the expression of molecules involved in axonal growth inhibition have reported similar beneficial effects on axonal growth. For example, the administration of monoclonal antibodies or peptide antagonists improves axonal and functional regeneration in rats subjected to spinal cord lesions [102-104]. An increase in the number of regenerated retinal ganglion cells axons passing through and growing beyond the injured optic nerve has also been described in an NgR double negative mutant model [105]. Recent studies also demonstrated that siRNA knockdown of p75NTR increases dorsal root ganglia neurite outgrowth in the presence of MAG [99], while the reduction of NgR expression levels using

Several authors have reported increased neurite outgrowth following RhoA inactivation, both *in vitro* [80,83,84,96,100,107] and *in vivo* [83,84]. In our study RhoA knockdown resulted in a greater expansion of the visual corticocollicular terminal field. Similarly, siRNA knockdown of p75NTR, NgR and most significantly, RhoA, was shown to disinhibit dorsal root ganglia neurite outgrowth in the presence of myelin [100]. It was suggested that in addition to myelin-derived inhibitory ligands, which act by binding to NgR, other neurite growth inhibitors including ephrins, semaphorins and CSPGs, may converge on the RhoA signaling pathway leading to growth cone collapse [108,109]. Thus, NgR knockdown may block the inhibitory action of myelin derived ligands alone, with no influence on other inhibitory ligands. Nonetheless, RhoA knockdown could block the convergent signaling

small hairpin RNAs augments axonal growth in neuronal cultures [106].

qRT-PCR in the cortex beneath the injection site.

respectively, relative to controls: Fig. 3).

from all inhibitory ligands.

(Fig. 1, 2).

Other strategies to promote axonal regeneration and reorganization following adult CNS lesions have been described in transgenic animal models. Nogo-A single knockout and Nogo-A/B double knockout mice exhibit dramatic increases in axonal sprouting and extension after spinal cord injury, accompanied by substantial locomotor recovery [92,93]. While no increase in axon regeneration was observed in another study in either Nogo-A/B double knockout or Nogo-A/B/C triple knockout mice [94], a more recent study using the optic nerve crush model in Nogo-A/B/C triple knockout mice reported significant axon regeneration [95], suggesting Nogo influences in axon regeneration *in vivo*.

Blockade of RhoA and ROCK activation with C3 transferase and Y-27632 antagonists, respectively, enhances axonal growth in myelin substrates *in vitro* [83,84,96] and *in vivo* [83,84]. However, the effectiveness of these antagonists appears to depend on their mode of administration, as C3 transferase was not effective in all *in vivo* studies [96]. Since the discovery of RNA interference [97], numerous studies have focused on inhibiting target molecules using siRNAs that specifically silence the expression of target mRNAs [98]. Several studies have reported the promotion of neurite outgrowth *in vitro* following siRNA administration. For example, siRNAs against p75NTR disinhibit dorsal root ganglia neurite outgrowth in the presence of MAG [99]. Likewise, siRNA-mediated silencing of components of the inhibitory signaling cascade, including p75NTR, NgR and RhoA mRNA, enhances dorsal root ganglia neurite outgrowth in the presence of CNS myelin, with RhoA knockdown exerting the strongest effect [100]. A recent study using a murine model of multiple sclerosis demonstrated that systematic administration of siRNAs against Nogo-A promotes functional recovery accompanied by a significant increase of GAP43 expression, a protein expressed in growing axons. Based on these findings, the authors suggested that axonal repair may underlie the improved clinical outcome in mice treated with siRNAs against Nogo-A [101].

## **5. Disinhibition of axonal growth by small interfering RNAs against the Nogo Receptor and RhoA**

Given the essential role of myelin-derived molecules in the inhibition of neurite outgrowth, we studied the effect of NgR and RhoA knockdown, key mediators of the signaling cascade that promotes actin depolymerization and subsequent growth cone collapse, and that triggers inhibition of axon growth [82]. We investigated whether these interventions result in the expansion of the corticocollicular connection in rats subjected to unilateral retinal deafferentation in adulthood, a response that normally only occurs when this lesion is induced neonatally. To this end, we administered a single injection of siRNAs against NgR or RhoA into the left primary visual cortex immediately after the enucleation of the right eye in two-month-old Sprague Dawley rats. After four days, the animals received a microinjection of the anterograde tracer BDA 10,000 at the site of siRNA administration. Seven days later the animals were perfused and the nervous tissue processed for histochemical analysis. Control rats received the same siRNA injections into the primary visual cortex. The effect of the siRNAs on NgR and RhoA mRNA levels were measured by qRT-PCR in the cortex beneath the injection site.

372 Visual Cortex – Current Status and Perspectives

against Nogo-A [101].

**Nogo Receptor and RhoA** 

antibodies in monkeys subjected to cervical spinal cord hemisection promotes extensive

Other strategies to promote axonal regeneration and reorganization following adult CNS lesions have been described in transgenic animal models. Nogo-A single knockout and Nogo-A/B double knockout mice exhibit dramatic increases in axonal sprouting and extension after spinal cord injury, accompanied by substantial locomotor recovery [92,93]. While no increase in axon regeneration was observed in another study in either Nogo-A/B double knockout or Nogo-A/B/C triple knockout mice [94], a more recent study using the optic nerve crush model in Nogo-A/B/C triple knockout mice reported significant axon

Blockade of RhoA and ROCK activation with C3 transferase and Y-27632 antagonists, respectively, enhances axonal growth in myelin substrates *in vitro* [83,84,96] and *in vivo* [83,84]. However, the effectiveness of these antagonists appears to depend on their mode of administration, as C3 transferase was not effective in all *in vivo* studies [96]. Since the discovery of RNA interference [97], numerous studies have focused on inhibiting target molecules using siRNAs that specifically silence the expression of target mRNAs [98]. Several studies have reported the promotion of neurite outgrowth *in vitro* following siRNA administration. For example, siRNAs against p75NTR disinhibit dorsal root ganglia neurite outgrowth in the presence of MAG [99]. Likewise, siRNA-mediated silencing of components of the inhibitory signaling cascade, including p75NTR, NgR and RhoA mRNA, enhances dorsal root ganglia neurite outgrowth in the presence of CNS myelin, with RhoA knockdown exerting the strongest effect [100]. A recent study using a murine model of multiple sclerosis demonstrated that systematic administration of siRNAs against Nogo-A promotes functional recovery accompanied by a significant increase of GAP43 expression, a protein expressed in growing axons. Based on these findings, the authors suggested that axonal repair may underlie the improved clinical outcome in mice treated with siRNAs

**5. Disinhibition of axonal growth by small interfering RNAs against the** 

Given the essential role of myelin-derived molecules in the inhibition of neurite outgrowth, we studied the effect of NgR and RhoA knockdown, key mediators of the signaling cascade that promotes actin depolymerization and subsequent growth cone collapse, and that triggers inhibition of axon growth [82]. We investigated whether these interventions result in the expansion of the corticocollicular connection in rats subjected to unilateral retinal deafferentation in adulthood, a response that normally only occurs when this lesion is induced neonatally. To this end, we administered a single injection of siRNAs against NgR or RhoA into the left primary visual cortex immediately after the enucleation of the right eye in two-month-old Sprague Dawley rats. After four days, the animals received a microinjection of the anterograde tracer BDA 10,000 at the site of siRNA administration. Seven days later the animals were perfused and the nervous tissue processed for

functional recovery, increased sprouting and regenerative axonal elongation [91].

regeneration [95], suggesting Nogo influences in axon regeneration *in vivo*.

Microinjection of siRNAs against NgR and RhoA into the primary visual cortex of adult enucleated rats promoted a mild expansion of the ipsilateral visual corticocollicular terminal field, although in both cases the centre of the field presented a characteristic column-like shape extending from the SO up to the pial surface, a similar pattern to that seen in nonsiRNA treated animals. Likewise, following siRNA injection, many fibers were observed running parallel to the pial surface, mainly located within the ventral half of the SGS and running away from the terminal field center towards the middle line. Moreover, several growth cone-bearing axons were observed in these cases, suggesting active axonal growth (Fig. 1, 2).

To confirm the inhibitory effect of siRNAs on NgR and RhoA mRNA expression in the primary visual cortex, and hence the involvement of these molecules in the reorganization of the visual corticocollicular field in adult rats subjected to retinal deafferentation, relative mRNA levels were quantified by qRT-PCR 24 hours after siRNA injection. This revealed significant decreases in NgR and RhoA mRNA levels (44.8 ± 7.3% and 21.67 ± 10.53%, respectively, relative to controls: Fig. 3).

These results demonstrate that siRNA-mediated abolition of the expression of key mediators of axonal growth inhibition, such as NgR and more notably RhoA, promotes axonal outgrowth after adult CNS injury. Indeed, recent studies using different approaches to reduce the expression of molecules involved in axonal growth inhibition have reported similar beneficial effects on axonal growth. For example, the administration of monoclonal antibodies or peptide antagonists improves axonal and functional regeneration in rats subjected to spinal cord lesions [102-104]. An increase in the number of regenerated retinal ganglion cells axons passing through and growing beyond the injured optic nerve has also been described in an NgR double negative mutant model [105]. Recent studies also demonstrated that siRNA knockdown of p75NTR increases dorsal root ganglia neurite outgrowth in the presence of MAG [99], while the reduction of NgR expression levels using small hairpin RNAs augments axonal growth in neuronal cultures [106].

Several authors have reported increased neurite outgrowth following RhoA inactivation, both *in vitro* [80,83,84,96,100,107] and *in vivo* [83,84]. In our study RhoA knockdown resulted in a greater expansion of the visual corticocollicular terminal field. Similarly, siRNA knockdown of p75NTR, NgR and most significantly, RhoA, was shown to disinhibit dorsal root ganglia neurite outgrowth in the presence of myelin [100]. It was suggested that in addition to myelin-derived inhibitory ligands, which act by binding to NgR, other neurite growth inhibitors including ephrins, semaphorins and CSPGs, may converge on the RhoA signaling pathway leading to growth cone collapse [108,109]. Thus, NgR knockdown may block the inhibitory action of myelin derived ligands alone, with no influence on other inhibitory ligands. Nonetheless, RhoA knockdown could block the convergent signaling from all inhibitory ligands.

The Experimental Manipulation of Visual Cortex Efferents 375

**Figure 2.** Photomicrographs of BDA-labeled visual corticocollicular terminal fields following

griseum superficiale; SO, stratum opticum. Scale bars = 100 µm (A,C) and 30 µm (B,D).

administration of siRNAs against NgR (A,B) and RhoA (C,D) in rats visually deafferented in adulthood. (A,C) The microinjection of siRNAs in the primary visual cortex, the projection origin, evoked an increase in terminal field extension, with fibers running towards the lateral SC and the medial edge. (B,D) Detail of axons in the SGS running away from the terminal field, some exhibiting terminal thickening (arrowheads), which may indicate the presence of vestigial growth cone. SGS, stratum

**Figure 3.** Relative expression of Nogo Receptor (A) and RhoA (B) mRNA in the primary visual cortex following administration of their corresponding siRNAs. Weaker NgR and RhoA mRNA expression in the siRNA-treated groups than in the untreated controls was detected. The relative mRNA levels are

normalized to YWHAZ and TATA box binding protein reference genes.

**Figure 1.** Scheme showing a dorsal view of the site of BDA injection into the primary visual cortex (left) and the projection site in the superior colliculus (right) in different experimental conditions. The administration of siRNAs against NgR and RhoA led to the expansion of the visual corticocollicular terminal field in animals subjected to retinal deafferentation in adulthood. The black areas in the SC denote regions with the greatest density of fibers, while the grey shaded areas denote regions of decreasing axonal density. M1VC, monocular primary visual cortex; B1VC, binocular primary visual cortex; c, caudal; m, medial; r, rostral. Scale bars = 2 mm (left) and 1 mm (right).

**Figure 1.** Scheme showing a dorsal view of the site of BDA injection into the primary visual cortex (left) and the projection site in the superior colliculus (right) in different experimental conditions. The administration of siRNAs against NgR and RhoA led to the expansion of the visual corticocollicular terminal field in animals subjected to retinal deafferentation in adulthood. The black areas in the SC denote regions with the greatest density of fibers, while the grey shaded areas denote regions of decreasing axonal density. M1VC, monocular primary visual cortex; B1VC, binocular primary visual

cortex; c, caudal; m, medial; r, rostral. Scale bars = 2 mm (left) and 1 mm (right).

**Figure 2.** Photomicrographs of BDA-labeled visual corticocollicular terminal fields following administration of siRNAs against NgR (A,B) and RhoA (C,D) in rats visually deafferented in adulthood. (A,C) The microinjection of siRNAs in the primary visual cortex, the projection origin, evoked an increase in terminal field extension, with fibers running towards the lateral SC and the medial edge. (B,D) Detail of axons in the SGS running away from the terminal field, some exhibiting terminal thickening (arrowheads), which may indicate the presence of vestigial growth cone. SGS, stratum griseum superficiale; SO, stratum opticum. Scale bars = 100 µm (A,C) and 30 µm (B,D).

**Figure 3.** Relative expression of Nogo Receptor (A) and RhoA (B) mRNA in the primary visual cortex following administration of their corresponding siRNAs. Weaker NgR and RhoA mRNA expression in the siRNA-treated groups than in the untreated controls was detected. The relative mRNA levels are normalized to YWHAZ and TATA box binding protein reference genes.

In summary, our *in vivo* results strongly support the use of siRNAs to silence inhibitors of axonal growth, promoting reorganization and axonal outgrowth of the visual corticocollicular connection in adult enucleated rats following a single siRNA injection.

The Experimental Manipulation of Visual Cortex Efferents 377

tracer injection, the animals were sacrificed and the nervous tissue analyzed histochemically. While a clear corticocortical projection from primary to secondary visual areas was observed in all cases, guanosine administration significantly increases the number and size of the presynaptic boutons along the axonal branches that reach the secondary visual areas, while the pattern of visual corticocortical projections was preserved. Laterally running fibers emerged at several different levels, white matter fibers ran close to layer VI, while at the level of layers VI and V, afferent fibers gave off divergent branches to form a dense plexus. A smaller contingent of corticocortical fibers ran horizontally along layers IV

and V, and a very superficial group of fibers ran laterally at the level of layer I [126].

astrocytic factors elicited by guanosine (Fig. 4) [126].

We observed 2 plexuses in this efferent connection, a deep plexus in layer IV-VI and another in the superficial layer I, both of which were connected by ascending fibers that gave off scarce divergent branches containing irregularly distributed presynaptic boutons. Treatment with guanosine either increased the number or altered the orientation of the axonal branches of the visual corticocortical connection. Moreover, the number and size of synaptic boutons was significantly higher in these animals, and most were more rounded/oval than those in control animals. Guanosine administration significantly increased bouton density (number/200 m2), which was 1.3-fold higher in treated versus control rats (p<0.02). Moreover, while the average size of small synaptic boutons did not appear to be affected by guanosine (0.57 + 0.07 m2 vs. 0.47 + 0.05 m2 in control animals; p<0.002), the larger boutons were significantly larger on average in guanosine-treated rats (3.76 + 0.06 m2 vs. 2.26 + 0.1 m2 in control rats; p<0.002). These data highlight the synaptogenic specificity of the

**Figure 4.** Representative photomicrographs of BDA-labeled visual corticocolicular terminal fields in control (A) and guanosine-treated rats (B). Note the markedly higher density of labeled presynaptic

We propose that synaptogenesis induced by the local application of guanosine *in vivo* may be mediated by factors such as cholesterol, ApoE and pleiotropic factors secreted by astrocytes. Guanosine administration increases both cholesterol and ApoE efflux from astrocytes *in vitro*, supporting a pharmacological role of guanosine in the modulation of cholesterol homeostasis in the brain [125]. Moreover, astrocytes that release guanosine can exert neurotrophic effects and promote neuritogenesis, possibly via MAP-kinase and PI3-

boutons in the guanosine-treated rat than in the control animals. Scale bar = 20 m.

## **6. Does guanosine enhance corticocortical synaptogenesis?**

Considerable effort has been directed towards identifying the specific molecules that guide axonal growth and subsequent synaptogenesis during development, some of which are inductive glial factors [110,111]. Evidence gathered over the last decade has attributed a fundamental role to astrocytes in regulating synaptogenesis and modulating synaptic plasticity during critical periods in different sensory and motor systems [112]. During postnatal development, astrocytes are strongly involved in the formation of synaptic contacts in the CNS, participating in each of the 3 stages of synaptogenesis: (i) the establishment of contacts between neurons; (ii) the formation of the synapse; and (iii) synaptic stabilization or elimination [113]. The role of astrocytes in regulating the synaptic stability of retinal ganglion cells (RGCs) has been studied in detail by culturing purified RGCs in the presence or absence of astrocytes [114]. Astrocytes promote an increase in the number of RGC synapses, although this effect is reversible since when cultured for one week after removing the glia there is a significant reduction in the number of synaptic puncta. The regulatory role of astrocytes in synaptogenesis has also been demonstrated through ultrastructural and physiological studies *in vivo*. Once retinocollicular afferents reach the collicular superficial strata, the synaptic arrangement is closely correlated with the growth and differentiation of astrocytes at the end of the first postnatal week in rats [114], or from P30-P40 in opposums [115].

Matricellular proteins are extracellular regulatory factors secreted by astrocytes that mediate cell-matrix interactions. This is heterogeneous group of proteins includes thrombospondins [116,117], HEVIN [118] and cholesterol [119], which are strongly expressed during development and in response to injury [120,121]. In addition, these matricellular proteins interact with different matrix constituents, growth factors, integrins and other cell surface receptors [122]. Co-culture of purified glutamatergic RGCs with astrocytes results in the secretion of cholesterol by glia, which promotes synaptogenesis [122]. The absence of glial cells from these cultures, or a reduction in the cholesterol content of glia-conditioned medium, diminishes both the number of synapses and GluR2/3 expression by RGCs [123]. While cholesterol production within the CNS is necessary for growth and survival, lipid raft signaling, synaptic vesicle formation and synaptic function [124], increased synaptogenesis and axon pruning requires additional cholesterol production [122]. Recent *in vitro* studies indicate that guanosine increases the efflux of cholesterol from astrocytes [125], the primary source of cholesterol in the nervous system. Moreover, binding of cholesterol to apolipoprotein E (ApoE) promotes synapse formation in RGC cultures [122]. We assessed the synaptogenic effect of guanosine administration *in vivo*, having anterogradely labeled the visual corticocortical connection in young adult male Sprague-Dawley rats by injecting BDA into the primary visual cortex. After BDA administration (24 hr), an osmotic pump was implanted at the site of BDA injection to administer guanosine (300 M) and 2 weeks after tracer injection, the animals were sacrificed and the nervous tissue analyzed histochemically. While a clear corticocortical projection from primary to secondary visual areas was observed in all cases, guanosine administration significantly increases the number and size of the presynaptic boutons along the axonal branches that reach the secondary visual areas, while the pattern of visual corticocortical projections was preserved. Laterally running fibers emerged at several different levels, white matter fibers ran close to layer VI, while at the level of layers VI and V, afferent fibers gave off divergent branches to form a dense plexus. A smaller contingent of corticocortical fibers ran horizontally along layers IV and V, and a very superficial group of fibers ran laterally at the level of layer I [126].

376 Visual Cortex – Current Status and Perspectives

from P30-P40 in opposums [115].

In summary, our *in vivo* results strongly support the use of siRNAs to silence inhibitors of axonal growth, promoting reorganization and axonal outgrowth of the visual corticocollicular connection in adult enucleated rats following a single siRNA injection.

Considerable effort has been directed towards identifying the specific molecules that guide axonal growth and subsequent synaptogenesis during development, some of which are inductive glial factors [110,111]. Evidence gathered over the last decade has attributed a fundamental role to astrocytes in regulating synaptogenesis and modulating synaptic plasticity during critical periods in different sensory and motor systems [112]. During postnatal development, astrocytes are strongly involved in the formation of synaptic contacts in the CNS, participating in each of the 3 stages of synaptogenesis: (i) the establishment of contacts between neurons; (ii) the formation of the synapse; and (iii) synaptic stabilization or elimination [113]. The role of astrocytes in regulating the synaptic stability of retinal ganglion cells (RGCs) has been studied in detail by culturing purified RGCs in the presence or absence of astrocytes [114]. Astrocytes promote an increase in the number of RGC synapses, although this effect is reversible since when cultured for one week after removing the glia there is a significant reduction in the number of synaptic puncta. The regulatory role of astrocytes in synaptogenesis has also been demonstrated through ultrastructural and physiological studies *in vivo*. Once retinocollicular afferents reach the collicular superficial strata, the synaptic arrangement is closely correlated with the growth and differentiation of astrocytes at the end of the first postnatal week in rats [114], or

Matricellular proteins are extracellular regulatory factors secreted by astrocytes that mediate cell-matrix interactions. This is heterogeneous group of proteins includes thrombospondins [116,117], HEVIN [118] and cholesterol [119], which are strongly expressed during development and in response to injury [120,121]. In addition, these matricellular proteins interact with different matrix constituents, growth factors, integrins and other cell surface receptors [122]. Co-culture of purified glutamatergic RGCs with astrocytes results in the secretion of cholesterol by glia, which promotes synaptogenesis [122]. The absence of glial cells from these cultures, or a reduction in the cholesterol content of glia-conditioned medium, diminishes both the number of synapses and GluR2/3 expression by RGCs [123]. While cholesterol production within the CNS is necessary for growth and survival, lipid raft signaling, synaptic vesicle formation and synaptic function [124], increased synaptogenesis and axon pruning requires additional cholesterol production [122]. Recent *in vitro* studies indicate that guanosine increases the efflux of cholesterol from astrocytes [125], the primary source of cholesterol in the nervous system. Moreover, binding of cholesterol to apolipoprotein E (ApoE) promotes synapse formation in RGC cultures [122]. We assessed the synaptogenic effect of guanosine administration *in vivo*, having anterogradely labeled the visual corticocortical connection in young adult male Sprague-Dawley rats by injecting BDA into the primary visual cortex. After BDA administration (24 hr), an osmotic pump was implanted at the site of BDA injection to administer guanosine (300 M) and 2 weeks after

**6. Does guanosine enhance corticocortical synaptogenesis?** 

We observed 2 plexuses in this efferent connection, a deep plexus in layer IV-VI and another in the superficial layer I, both of which were connected by ascending fibers that gave off scarce divergent branches containing irregularly distributed presynaptic boutons. Treatment with guanosine either increased the number or altered the orientation of the axonal branches of the visual corticocortical connection. Moreover, the number and size of synaptic boutons was significantly higher in these animals, and most were more rounded/oval than those in control animals. Guanosine administration significantly increased bouton density (number/200 m2), which was 1.3-fold higher in treated versus control rats (p<0.02). Moreover, while the average size of small synaptic boutons did not appear to be affected by guanosine (0.57 + 0.07 m2 vs. 0.47 + 0.05 m2 in control animals; p<0.002), the larger boutons were significantly larger on average in guanosine-treated rats (3.76 + 0.06 m2 vs. 2.26 + 0.1 m2 in control rats; p<0.002). These data highlight the synaptogenic specificity of the astrocytic factors elicited by guanosine (Fig. 4) [126].

**Figure 4.** Representative photomicrographs of BDA-labeled visual corticocolicular terminal fields in control (A) and guanosine-treated rats (B). Note the markedly higher density of labeled presynaptic boutons in the guanosine-treated rat than in the control animals. Scale bar = 20 m.

We propose that synaptogenesis induced by the local application of guanosine *in vivo* may be mediated by factors such as cholesterol, ApoE and pleiotropic factors secreted by astrocytes. Guanosine administration increases both cholesterol and ApoE efflux from astrocytes *in vitro*, supporting a pharmacological role of guanosine in the modulation of cholesterol homeostasis in the brain [125]. Moreover, astrocytes that release guanosine can exert neurotrophic effects and promote neuritogenesis, possibly via MAP-kinase and PI3-

kinase signaling pathways [127]. Previous studies failed to report an increase in synapse number in response to cholesterol administration *in vitro* [116], despite a strong enhancement in synaptic efficacy [119]. However, guanosine increased both the number or size of synaptic boutons in our *in vivo* model. These morphological changes were observed at least one week after 7 days of local guanosine administration and it is likely that this effect progressively diminishes over subsequent days, as is usually observed in nervous structures following lesion. Nonetheless, the synaptogenic effects promoted by reactive astrocytes in denervated terminal fields can last for months [34]. The larger synaptic boutons generated following guanosine administration may reflect the accumulation of presynaptic components such as mitochondria, synaptic vesicles and presynaptic receptors, elements that could eventually exert a modulatory effect upon functional aspects of neurotransmission, such as transmitter release or presynaptic potentiation. It remains unclear whether the proportion of synapses that contact neurons varies after guanosine administration, as astrocytes promote cholesterol-mediated glutamatergic synaptogenesis but they induce GABAergic synaptogenesis via a different mechanism [128], raising the possibility that the majority of new synapses are glutamatergic. Changes in the proportion of inhibitory and excitatory synapses may trigger homeostatic mechanisms [129] that maintain the synaptic activity of the connection within certain functional limits.

The Experimental Manipulation of Visual Cortex Efferents 379

significant post-lesional sprouting of these neurons following specific siRNA knockdown of molecules that inhibit axon regeneration. This strategy is particularly efficacious on a broad range of potential targets. The combination of this knockdown approach with strategies to promote axonal growth by trophic stimuli may be particularly promising for the therapeutic

*Dept. of Neurosciences, Faculty of Medicine, University of the Basque Country, Leioa, Bizkaia, Spain* 

Grant sponsors: Fondo de Investigaciones Sanitarias (Ministerio de Sanidad y Consumo) PI05/2046 and PS09/00476; Universidad del País Vasco/Euskal Herriko Unibertsitatea GIU06/15; Gobierno Vasco SA-2010/00095 and GIC10/113; UPV/EHU Predoctoral Fellowship

[1] Zilles K, Zilles B, Schleicher A (1980) A Quantitative Approach to Cytoarchitectonics. VI. The Areal Pattern of the Cortex of Albino Rat. Anat. embryol. 159: 335-360. [2] Zilles K, Wree A (1995) Cortex: Areal and Laminar Structure. In: Paxinos G, editors. The

[3] Dreher B, Thong IG, Shameem N, McCall MJ (1985) Development of Cortical Afferents and Cortico-tectal Efferents of the Mammalian (Rat) Primary Visual Cortex. Aust. n. z. j.

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[7] Huerta MF, Harting JK (1984) Connectional Organization of the Superior Colliculus.

Technical and human support from SGIker is gratefully acknowledged.

Rat Nervous System. San Diego: Academic press. pp. 649-685.

Pigmented and Albino Mice. J. comp. neurol. 191: 383-412.

modulation of specific neuronal connections in the future.

I. Gerrikagoitia, B. Rienda and L. Martínez-Millán\*

**Author details** 

**Acknowledgement** 

PIFA/01/2006/042.

**8. References** 

ophthalmol. 13: 251-261.

Brain behave. evol. 26: 10-48.

Trends Neurosci. 7: 286-289.

350: 439-451.

Corresponding Author

 \*

Synaptogenesis occurs both during development and adult life. In addition to the aforementioned factors, several other factors promote synaptogenesis in mature nervous systems, including GDNF (glial derived neurotrophic factor) and sex hormones, particularly in areas that display strong synaptic plasticity [130,131]. *In vivo* studies of the neocortex [132,133] revealed the ongoing growth and retraction of dendritic spines, accompanied by the elimination and formation of synapses. While we have only examined labeled projecting axons, synaptogenic effects may also extend to axons emerging from interneurons. Therefore, it may be of interest to analyze GABAergic synaptogenesis in the area surrounding the site of implantation of the osmotic pump that supplies guanosine in this experimental paradigm.

The increase in the number and size of a significant proportion of synapses after guanosine administration indicates a potentiation of axon growth that may promote reinnervation after partial experimental lesion of a neural pathway, or after elimination of a specific afferent connection projecting to a given brain region. We are currently investigating other strategies to inhibit molecules that restrict axonal sprouting and regeneration, including the injection of siRNAs against the p75 receptor and LINGO-1 into the contralateral visual cortex following monocular retinal deafferentation, with encouraging preliminary results.

## **7. Conclusion**

In contrast to the classical dogma of neuronal regeneration, the results presented here indicate that both corticocortical and corticosubcortical connections can be manipulated in adult animals. We focused specifically on two connections, namely corticocollicular and corticocortical projections emerging from the primary visual cortex, and we demonstrate significant post-lesional sprouting of these neurons following specific siRNA knockdown of molecules that inhibit axon regeneration. This strategy is particularly efficacious on a broad range of potential targets. The combination of this knockdown approach with strategies to promote axonal growth by trophic stimuli may be particularly promising for the therapeutic modulation of specific neuronal connections in the future.

## **Author details**

378 Visual Cortex – Current Status and Perspectives

experimental paradigm.

**7. Conclusion** 

kinase signaling pathways [127]. Previous studies failed to report an increase in synapse number in response to cholesterol administration *in vitro* [116], despite a strong enhancement in synaptic efficacy [119]. However, guanosine increased both the number or size of synaptic boutons in our *in vivo* model. These morphological changes were observed at least one week after 7 days of local guanosine administration and it is likely that this effect progressively diminishes over subsequent days, as is usually observed in nervous structures following lesion. Nonetheless, the synaptogenic effects promoted by reactive astrocytes in denervated terminal fields can last for months [34]. The larger synaptic boutons generated following guanosine administration may reflect the accumulation of presynaptic components such as mitochondria, synaptic vesicles and presynaptic receptors, elements that could eventually exert a modulatory effect upon functional aspects of neurotransmission, such as transmitter release or presynaptic potentiation. It remains unclear whether the proportion of synapses that contact neurons varies after guanosine administration, as astrocytes promote cholesterol-mediated glutamatergic synaptogenesis but they induce GABAergic synaptogenesis via a different mechanism [128], raising the possibility that the majority of new synapses are glutamatergic. Changes in the proportion of inhibitory and excitatory synapses may trigger homeostatic mechanisms [129] that

maintain the synaptic activity of the connection within certain functional limits.

Synaptogenesis occurs both during development and adult life. In addition to the aforementioned factors, several other factors promote synaptogenesis in mature nervous systems, including GDNF (glial derived neurotrophic factor) and sex hormones, particularly in areas that display strong synaptic plasticity [130,131]. *In vivo* studies of the neocortex [132,133] revealed the ongoing growth and retraction of dendritic spines, accompanied by the elimination and formation of synapses. While we have only examined labeled projecting axons, synaptogenic effects may also extend to axons emerging from interneurons. Therefore, it may be of interest to analyze GABAergic synaptogenesis in the area surrounding the site of implantation of the osmotic pump that supplies guanosine in this

The increase in the number and size of a significant proportion of synapses after guanosine administration indicates a potentiation of axon growth that may promote reinnervation after partial experimental lesion of a neural pathway, or after elimination of a specific afferent connection projecting to a given brain region. We are currently investigating other strategies to inhibit molecules that restrict axonal sprouting and regeneration, including the injection of siRNAs against the p75 receptor and LINGO-1 into the contralateral visual cortex

In contrast to the classical dogma of neuronal regeneration, the results presented here indicate that both corticocortical and corticosubcortical connections can be manipulated in adult animals. We focused specifically on two connections, namely corticocollicular and corticocortical projections emerging from the primary visual cortex, and we demonstrate

following monocular retinal deafferentation, with encouraging preliminary results.

I. Gerrikagoitia, B. Rienda and L. Martínez-Millán\* *Dept. of Neurosciences, Faculty of Medicine, University of the Basque Country, Leioa, Bizkaia, Spain* 

## **Acknowledgement**

Grant sponsors: Fondo de Investigaciones Sanitarias (Ministerio de Sanidad y Consumo) PI05/2046 and PS09/00476; Universidad del País Vasco/Euskal Herriko Unibertsitatea GIU06/15; Gobierno Vasco SA-2010/00095 and GIC10/113; UPV/EHU Predoctoral Fellowship PIFA/01/2006/042.

Technical and human support from SGIker is gratefully acknowledged.

## **8. References**


<sup>\*</sup> Corresponding Author

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**Chapter 18** 

© 2012 Supèr et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Role of Feedforward and Feedback Projections** 

In the visual brain incoming sensory information is first decomposed into elementary features in low-level areas and then transferred to high-level areas. There the features are grouped into coherent perceptual representations. Recent findings, however, have established that stimulus evoked responses in the primary visual cortex are modulated by surrounding stimuli. The modulated responses depend on proper recurrent interactions between different, separate visual regions. These extra-classical receptive field responses combine local visual signals with more global information from the visual scene and often reflect relatively high-level perceptual attributes of the stimuli. One of the fundamental problems to be solved by the visual system is the segregation of figure from ground (see Figure 1). A key factor in the figure-ground process is the combination of local with global information. Therefore, contextual influences on neuronal activity have been interpreted as

The visual brain is considered to be hierarchically structured. From the retina most information flows to the primary visual cortex (also referred to as striate cortex, V1, or E17) through the thalamic lateral geniculate nucleus (LGN). In V1 neurons extract simple, rather abstract features (e.g. orientation) within a small part of the visual scene. The feature information is further conveyed to surrounding extra-striate areas and from there to the higher level visual areas. In fact, the feedforward projection is dichotomized into two streams. Axons projecting towards areas in the temporal lobe define the ventral pathway (also called as the "what" or "perception" stream) and projections to the parietal areas form the dorsal pathway (also called the "where" or "action" stream). Information flowing to the

and reproduction in any medium, provided the original work is properly cited.

**in Figure-Ground Responses** 

Marina Arall, August Romeo and Hans Supèr

Additional information is available at the end of the chapter

the neural substrate of figure-ground perception.

**2. Feedforward projections in the visual system** 

http://dx.doi.org/10.5772/47753

**1. Introduction** 

