Computational Modeling of Neural Plasticity for Adaptive Cognitive Enhancement Systems

Abstract

The neural plasticity, the process through which the brain restructures itself and changes its functioning in reaction to experience, is a key factor in the process of learning, memory and cognitive adaptation. The modeling of such adaptive mechanisms is now possible due to recent developments in computational methods which provide new opportunities to cognitive enhancement systems. The research will be focused on the derivation of a model of neural plasticity that can be applied to achieve adaptive cognitive enhancement through the dynamical adjustment of the user performance and environmental stimuli. The suggested model incorporates the principles of bio-inspired plasticity with machine learning, allowing the adaptive weight adjustment and feedback-based mechanisms of learning. A simulation-based model was conducted to test the model in enhancing cognitive task performance with time. Learning efficiency, adaptability and accuracy of response in different conditions were key performance indicators. Experimental findings indicate that the model proposed is able to show considerable progress in speed and adaptability of learning over the baseline methods. The system responds well to the varied input, and it resembles fundamental features of biological neural plasticity and optimizes the performance in various tasks. Although there are some limitations to the present study, it shows that computational neural plasticity models can be used to produce intelligent, adaptive cognitive enhancement systems. The results help advance the current field of computational neuroscience and can be used in practice to support personalized learning, neurorehabilitation and adaptive artificial intelligence systems.