Modeling multiple classical conditioning mechanisms in a Memristor-Based learning circuit

To achieve circuit-level modeling of multiple complex associative learning mechanisms in brain-inspired systems, this paper proposes an analog neural circuit based on threshold memristors, capable of emulating multiple classical conditioning phenomena in a biologically inspired manner. Specifically, the circuit incorporates multimodal sensory stimuli, including gustatory, visual, and auditory signals, to construct biologically inspired associative pathways. Through dynamical synaptic regulation by memristors across multiple neural pathways, the proposed rate-based analog circuit successfully reproduces several classical conditioning processes, such as acquisition, second-order conditioning, overshadowing, blocking, extinction, and reacquisition. It is noted that the circuit adopts a rate-based analog design paradigm, in which neuronal activation is governed by threshold comparison of weighted voltage summations, rather than spike-timing dynamics as in biologically accurate spiking neural networks. Moreover, the second-order conditioning pathway and the synaptic competition underlying overshadowing and blocking are validated through PSPICE simulations, demonstrating the synaptic plasticity of threshold memristors in modeling higher-order associative learning. Unlike prior single-mechanism approaches, this circuit realizes multiple associative processes within a unified memristive framework, extending memristor use in brain-inspired computing and cognitive hardware modeling.