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While Artificial Neural Networks (ANNs) have yielded impressive results inthe realm of simulated intelligent behavior, it is important to remember thatthey are but sparse approximations of Biological Neural Networks (BNNs). We gobeyond comparison of ANNs and BNNs to introduce principles from BNNs that mightguide the further development of ANNs as embodied neural models. Theseprinciples include representational complexity, complex networkstructure/energetics, and robust function. We then consider these principles inways that might be implemented in the future development of ANNs. Inconclusion, we consider the utility of this comparison, particularly in termsof building more robust and dynamic ANNs. This even includes constructing amorphology and sensory apparatus to create an embodied ANN, which whencomplemented with the organizational and functional advantages of BNNs unlocksthe adaptive potential of lifelike networks.

Connectionism, Complexity, and Living Systems: a comparison of Artificial and Biological Neural Networks