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

• Published in: IEEE transactions on cybernetics Vol. 53; no. 5; pp. 1 - 12
• Main Authors: Sun, Junwei, Wang, Yangyang, Liu, Peng, Wen, Shiping, Wang, Yanfeng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Associative memory; Brain; Circuit design; Conditioning (learning); Conditioning, Classical; Differentiation; Differentiation inhibition; Learning; memristor; Memristors; Modules; multiple generalization and differentiation; Neural networks; Neural Networks, Computer; Neurons; Neurons - physiology; secondary differentiation
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2022.3200751

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.