Memristor-based neuromorphic circuit based on associative network theory of mood and memory: A bio-Inspired approach...

Most memristor-based circuits that incorporate emotion effects focus on simple learning and recall behaviors, overlooking the formation of emotion-target associations during learning and their subsequent influence on recall under varying emotional states-phenomena well explained by associative network theory. This work presents a memristor-based circuit whose emotional dynamics are interpretable by associative network theory, explicitly accounting for how different learning conditions and recall emotions modulate recall performance. The designed circuit consists of an emotion module, a learning classification module, a memory module, a recall evaluation module and a recall performance module. The emotion module realizes the generation, recovery and inhibition of emotions. The learning classification and memory modules achieve mood-congruent memory (MCM), short-term and long-term memory, faster learning and slower forgetting and the establishment of emotional associations. The recall evaluation and recall performance modules then quantify how learning contexts and emotional states affect retrieval, demonstrating mood-dependent memory (MDM). All functions are validated through detailed circuit simulations. This work offers a conceptual framework validation for intelligent affective bionic robots and provides new insights for brain-inspired intelligence.