A Memristor-Based Neural Network Circuit with Classical Conditioning and Fear Generalization

Abstract

The new generation of artificial intelligence (AI) is evolving from perception-oriented processing toward cognition-oriented intelligence, emphasizing learning, memory, decision-making, and adaptive behavior inspired by the human brain. In this paper, a memristor-based perceptual decision-making neural network circuit (PDNNC) is proposed to integrate multiple classical conditioning mechanisms and fear generalization within a unified hardware framework. Unlike existing studies that focus on isolated learning behaviors, the proposed circuit architecture simultaneously realizes associative learning, forgetting, latent inhibition, blocking effects, secondary conditioning, and emotion-modulated decision-making at the circuit level. A voltage-controlled threshold-type memristor model is employed to emulate synaptic plasticity, while modular perceptual and decision-making subcircuits are designed to ensure scalability and functional consistency. The complete circuit implementation is verified using PSPICE circuit simulations. Simulation results demonstrate the correct realization of diverse classical conditioning behaviors, controllable fear generalization, and adaptive decision responses under different perceptual stimuli. The proposed bio-inspired circuit has vast potential applications in realizing cognitive intelligence and robotics.