Memristor-Based Neural Network Circuit With Multimode Generalization and Differentiation on Pavlov Associative Memory

Most of the classical conditioning laws implemented by existing circuits are involved in learning and forgetting between only three neurons, and the problems between multiple neurons are not considered. In this article, a multimode generalization and differentiation circuit for the Pavlov associative memory is proposed based on memristors. The designed circuit is mainly composed of voltage control modules, synaptic neuron modules, and inhibition modules. The secondary differentiation is accomplished through the process of associative learning and forgetting among multiple neurons. The process of multiple generalization and differentiation is realized based on the nonvolatility and thresholding properties of memristors. The extinction inhibition and differentiation inhibition in forgetting is considered through the inhibition modules. The Pavlov associative memory neural network with multimodal generalization and differentiation may provide a reference for the further development of brain-like intelligence.

Automated summary

In this article, a multimode generalization and differentiation circuit for Pavlov associative memory is proposed using memristors. The circuit is designed to consider the learning and forgetting processes among multiple neurons, and achieves secondary differentiation. The nonvolatility and thresholding properties of memristors are utilized for multiple generalization and differentiation, while inhibition modules are used for extinction and differentiation inhibition during forgetting.