Enhancing neural rehabilitation insights: on the path of bridging artificial and biological neural networks

This paper introduces the conceptual parallel between the ANN training process and the learning mechanisms of the human brain. Then, we briefly discuss a set of recently achieved experimental findings from a prior study that delves into various scenarios, aiding in comprehending the functionality of impaired or damaged neurons within a neural system. The key contribution of this paper is to present a novel variant of the Adam optimizer that incorporates a dynamic momentum adjustment factor, adaptive learning rate, and elastic weight consolidation technique. This enhanced version draws inspiration from biological processes to improve learning stability in artificial neural networks, with conceivable relevance to neural adaptation and rehabilitation research.