Neuro-Adaptive Learning: Merging Neuroscience and AI for Human-Like Decision Making

Abstract

The goal of the innovative method known as neuro-adaptive learning is to create models that can make decisions similar to humans by combining ideas from neuroscience with AI. This method improves AI's responsiveness to novel situations by modeling its learning and adaptation after the brain's processes. It does this by drawing on neural systems like attention, reinforcement learning, and synaptic plasticity. learning efficiency, adaptability, and decision-making in artificial intelligence systems by using the fundamental principles of neuro-adaptive learning. We go over the major developments in neuroscience, such as models of the neural circuitry and reward system of the human brain, that have served as inspiration for adaptive learning algorithms. We show, via tests in dynamic settings, that neuro-adaptive models are superior to conventional AI methods, especially for tasks that demand multi-task learning, context awareness, and real-time adaptation. Neuro-adaptive learning has the ability to unite human thinking with artificial intelligence, paving the way for systems that can adapt and make better decisions in complicated settings.