Memristor-Based Reward and Punishment Neural Network Circuit With Approach and Inhibition and Its Application in Industrial Vehicle Autonomous Navigation

Abstract

Current memristive circuits only focus the impact of simple rewards and punishments on biological behaviors, without considering the consequences of sustained stimuli and the occurrence of secondary behaviors. In this article, a memristor-based reward and punishment neural network circuit with approach and inhibition is designed, secondary behaviors are taken into account. The designed circuit is mainly composed of thalamus module, reward pathway, punishment pathway, amygdala module, feature module, and prefrontal cortex module. The signal processing in the brain is simulated by reward and punishment neural network, where signals of different intensities are produced to generate different overshadowing effects. Continuous stimulus is generated by external signal, producing different emotions and affecting memory. Approach and inhibition behaviors are initial outcomes, followed by secondary behaviors by competition between systems. The feasibility of the circuit is verified by PSpice, the proposed circuit provides a reference for biomimetic robots in neurocomputing and industrial applications.