**4. Autonomy**

Our approach to autonomous robots is based in autonomous controllers, constructed with artificial neural nets, and incorporating basic rules of living brains in terms of energy usage, default mode network (DMN), and the orchestrated, autonomous transitions forms default mode to other operative behaviors in reaction to stimuli.

#### **4.1. Artificial neural networks**

The neural networks used in this chapter are sigmoidal neurons trained with backpropagation [17]. Other functioning details are given in [12, 13].

In both biology and circuit complexity theory, it is maintained that deep architectures can be much more efficient (even exponentially efficient) than shallow ones in terms of computational power and abstract representation of some functions [18, 19]. Unfortunately, wellestablished gradient descent methods such as backpropagation that have proven effective when applied to shallow architectures do not work well when applied to deep architectures. Our method uses shallow nets trained with backpropagation, but these networks are thereafter stacked with other networks, thus becoming deep architectures.
