Memristor-Based Neural Network Circuit of Operant Conditioning With Bridging and Conditional Reinforcement

Most memristor-based neural network circuits consider only a single pattern of classical conditioning (CC) or operant conditioning (OC), but the simultaneous occurrence of CC and OC during actual animal training is ignored. In this paper, a memristor-based neural network circuit of operant conditioning with bridging and conditional reinforcement is designed. CC and OC can occur simultaneously and multiple CC and OC functions are considered. The designed circuit mainly consists of memory module, delay module, prefrontal cortex module, experience module and generalization module. Bridging in OC is implemented by the delay module and the prefrontal cortex module. Conditional reinforcement in OC is realized by the memory module and the prefrontal cortex module. Finally, the generalization of OC is achieved through the generalization module and the experience module. The proposed circuit may inform the study of smarter brain-like systems.