Memristor-Based Neural Network Circuit of Full-Function Pavlov Associative Memory With Unconditioned Response Mechanisms

Abstract

Most memristor-based Pavlovian associative memory neural networks explore the impact of relearning and forgetting on associative memory rates. In this paper, a memristor-based neural network circuit with unconditioned response mechanisms is proposed. The circuit is composed of neuron modules, synapse modules, forgetting voltage control modules, and effective time judgment modules. The proposed circuit achieves control of the forgetting rate, which can be adjusted based on the interval of the unconditioned stimuli. Additionally, the concept of effective time judgment has been introduced. If the stimulus interval exceeds the range of effective time, the circuit will automatically proceed with natural forgetting. Furthermore, the synaptic weight between the food neuron and the salivation neuron is no longer fixed but can change based on the method of food presentation. When only looking at the food but not eating it, the connection between the visual neuron and the salivation neuron decreases, resulting in reduced salivation when seeing the food. However, after eating the food, the connection between the visual neuron and the salivation neuron quickly strengthens. This circuit further refines the functionality of neural network circuits based on the mechanisms of unconditional reflexes. Finally, these functions are validated using PSPICE.