**8.2 Neuromorphic applications**

### *8.2.1 Medicine*

The ability of neuromorphic devices to receive and process information from their surroundings is particularly effective [282–286]. These devices can work with the human body when combined with organic components. Neuromorphic devices may enhance drug delivery techniques in the future. Due to their high responsiveness, they may release a medicine when they noticed a change in the body's conditions (i.e. varying insulin and glucose levels). The employment of neuromorphic computer technology in prostheses is also possible. Another advantage of this technology is its capability to efficiently accept and process an external signal. For those with prosthesis, using neuromorphic devices rather than conventional ones could result in a more natural, seamless experience [282].

*Neuromorphic Computing between Reality and Future Needs DOI: http://dx.doi.org/10.5772/intechopen.110097*

#### *8.2.2 Large-scale operations and product customization*

Neuromorphic computing may also be useful for large-scale initiatives and product customization [282]. It could be used to handle massive amounts of data from environmental sensors more quickly. Depending on the requirements of the sector, these sensors could monitor water content, temperature, radiation, and other characteristics. By identifying patterns in these data, the neuromorphic computing framework might make it simpler to draw useful conclusions. Due to the characteristics of the materials used to construct them, neuromorphic devices may potentially facilitate product customisation. These substances can be turned into fluids that are simple to control. They can be processed through additive manufacturing in liquid form to produce devices that are especially suited to the requirements of the user [282].

#### *8.2.3 Artificial intelligence*

By definition, the goal of neuromorphic computing is to replicate how the human brain works. Neurons in the brain receive, process, and transmit impulses in a very quick and energy-efficient manner. As a result, it makes sense that technology experts, particularly those working in the area of artificial intelligence (AI), would be fascinated by this kind of computing. As the name implies, experts in the field of artificial intelligence (AI) concentrate on a certain aspect of brain intelligence. The capacity of the intellect to gather and use knowledge is known as intelligence. It would be advantageous for the two fields to work together moving forward because this concept is so closely related to neuromorphic computing [287]. The answer to creating computers with truly human-level intelligence may lie in concentrating on brain functionality at the level of an artificial neuron and smaller.

#### *8.2.4 Imaging*

Similar to how the human eye creates images, neuromorphic vision sensors do the same. They are event-based imaging devices [288]. This demonstrates that they create images in response to light intensity which is an external signal rather than an internal signal [289]. Furthermore, they move at a faster pace independent of conventional frame rates. In a neuromorphic sensor, each pixel functions independently of its neighbours. Additionally, the gadget almost instantly communicates changes to each pixel [288]. These mechanisms work together to make data utilisation significantly more effective. Like their traditional equivalents, these sensors do not exhibit motion blur or a delayed response to the environment. Incorporating neuromorphic vision sensors into virtual and augmented reality systems may be advantageous given these qualities.

#### *8.2.5 Other applications*

Neuromorphic computing may be suited for use on "the edge" due to its low energy consumption [290]. The boundary of a network where a device could connect to a cloud platform is referred to as "the edge." Since driverless cars must operate in this environment, neuromorphic computing may enable them to react to their surroundings more quickly. A system using neuromorphic computing could take control of these vehicles when they are not linked to a reliable internet source. This might increase the safety and environmental suitability of driverless cars. Neuromorphic computing's

superior sensory capabilities may also enhance current "smart technologies" [283]. This modification might improve the effectiveness of the devices for a wider range of circumstances, similar to driverless cars. Expanding communication channels is another potential application that could be done using neuromorphic computing.
