A Review of Reinforcement Learning in Neuromorphic VLSI Chips using Computational Cognitive Neuroscience

Abstract: In this review paper, Cognitive models are used to implement the Reinforcement Learning in Neuromorphic VLSI Chips, to exhibit intelligence when the machines are exposed to an undefined Situation or field so as to achieve maximum rewards for taking right decision or very feasible solution or optimal path. This is done by modelling the attention and perception of machine just as a human being. Because the thought process is the unique nature of humans‟ intelligence can be implemented by modelling the Cognition. Comparison with other methods of attaining Artificial Intelligence shows that Computational Cognitive Neuroscience is the best and most evolved system for exhibiting intelligence to learn in real time scenarios. It is concluded that all the sensory input is not necessarily being calculated, instead attention is given to effective perception and trivial perception is ignored. This differentiation of perception is done based on BDI Model. Intelligence is broadly defined and requirements of an intelligent agent is summarized. The emerging field of electronics for implementation of reinforcement learning by imitating human brain i.e. Neuromorphic Engineering is discussed. The significance of the accurate knowledge of intelligence in machines based on learning and decision making is discussed.

Keywords: cognitive sciences, computational intelligence, Reinforcement Learning, Intelligent systems, Neuromorphic Engineering.