A Memristor-Based Neural Network Circuit With Retrospective Revaluation Effect and Application in Intelligent Household Robots

Abstract

The traditional association theory maintains that associations between cues can change only in trials where the cue is actually presented. However, the retrospective revaluation (RR) studies the phenomenon that responses to a cue can change even when the cue is not actually presented. A hardware memristor-based neural network circuit with an RR effect is proposed in this article. The neural network circuit successfully demonstrates various phenomena of RR, including the impact of deflation and inflation of companion cue associations on target cue, higher order RR, and context dependence. The correctness of the circuit design is verified by Pspice simulation. The key feature of this design lies in its ability to learn cue associations even in training trials, where the target cues are absent. This distinctive attribute offers a fresh perspective for the creation of more intricate, brain-inspired information processing systems with enhanced integration capabilities.