Connectionism, Complexity, and Living Systems: a comparison of Artificial and Biological Neural Networks
This is an incremental contribution for AI researchers seeking to improve neural network robustness by drawing inspiration from biological systems.
The paper tackles the gap between artificial and biological neural networks by proposing principles from biology to enhance ANNs, aiming to build more robust and dynamic systems through embodied models.
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.