  While Artificial Neural Networks (ANNs) have yielded impressive results in
the realm of simulated intelligent behavior, it is important to remember that
they are but sparse approximations of Biological Neural Networks (BNNs). We go
beyond comparison of ANNs and BNNs to introduce principles from BNNs that might
guide the further development of ANNs as embodied neural models. These
principles include representational complexity, complex network
structure/energetics, and robust function. We then consider these principles in
ways that might be implemented in the future development of ANNs. In
conclusion, we consider the utility of this comparison, particularly in terms
of building more robust and dynamic ANNs. This even includes constructing a
morphology and sensory apparatus to create an embodied ANN, which when
complemented with the organizational and functional advantages of BNNs unlocks
the adaptive potential of lifelike networks.
