The cost of unmodeled biological complexity in artificial neural networks

We propose the theories of pragmatism and functionalism to differentiate between artificial neural networks (ANNs) and biological neural networks (BNNs). While ANNs emulate some cell structures and function approximation mechanisms, questions remain about their ability to emulate intelligent behavior observed in BNNs. We propose that relying solely on biological structures suitable for function approximation may overlook pivotal aspects of ANNs' development, limiting their potential to emulate robust intelligence. Specifically, we investigate the role of ion channels in biological neurons and the randomness they introduce. This randomness seems to be vital for spike generation, although it is not directly related to function approximation. We conclude that structures, which do not directly contribute to function approximation, play a significant role in controlled activity, such as behavior, and should be integrated more into the controlled activity of artificial systems.