Brain-Inspired Reservoir Computing Models

Abstract

AbstractThis chapter presents an overview of brain-inspired reservoir computing models for sensory-motor information processing in the brain. These models are based on the idea that the brain processes information using a large population of interconnected neurons, where the dynamics of the system can amplify, transform, and integrate incoming signals. We discuss the reservoir predictive coding model, which uses predictive coding to explain how the brain generates expectations regarding sensory input and processes incoming signals. This model incorporates a reservoir of randomly connected neurons that can amplify and transform sensory inputs. Moreover, we describe the reservoir reinforcement learning model, which explains how the brain learns to make decisions based on rewards or punishments received after performing a certain action. This model uses a reservoir of randomly connected neurons to represent various possible actions and their associated rewards. The reservoir dynamics allow the brain to learn which actions lead to the highest reward. We then present an integrated model that combines these two reservoir computing models based on predictive coding and reinforcement learning. This model demonstrates how the brain integrates sensory information with reward signals to learn the most effective actions for a given situation. It also explains how the brain uses predictive coding to generate expectations about future sensory inputs and accordingly adjusts its actions. Overall, brain-inspired reservoir computing models provide a theoretical framework for understanding how the brain processes information and learns to make decisions. These models have the potential to revolutionize fields such as artificial intelligence and neuroscience, by advancing our understanding of the brain and inspiring new technologies.