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

TL;DR

This paper compares artificial and biological neural networks, proposing principles from BNNs to guide the development of more robust, embodied, and adaptive ANNs, emphasizing complexity, structure, and function.

Contribution

It introduces principles from biological neural networks to inform the design of advanced artificial neural networks, focusing on embodiment and robustness.

Findings

BNNs exhibit higher representational complexity.

Structured network energetics enhance robustness.

Embodied ANNs can unlock adaptive potential.

Abstract

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.