Bienenstock, Cooper, and Munro Learning Rules Realized in Second‐Order Memristors with Tunable Forgetting Rate

TLDR

Memristors with synaptic functions are promising for artificial neural networks, yet existing BCM rule implementations lack a tunable sliding frequency threshold because the memristors’ forgetting rates are not adjustable. This study aims to biorealistically implement BCM learning rules with a tunable sliding frequency threshold in SrTiO₃‑based second‑order memristors. The forgetting rate is tuned by engineering the electrode/oxide interface through electrode composition control, enabling the desired BCM behavior. The resulting implementation is precise and efficient, enhancing neural network performance in artificial intelligence systems.

Abstract

Abstract Memristors with synaptic functions are very promising for developing artificial neural networks. Compared with the extensively reported spike‐timing‐dependent plasticity (STDP), Bienenstock, Cooper, and Munro (BCM) learning rules, the most accurate model of the synaptic plasticity to date, are more compatible with the neural computing system; however, the progress in the realization of the BCM rules has been quite limited. The realized BCM rules so far mostly performs just the spike‐rate‐dependent plasticity (SRDP), however, without a tunable sliding frequency threshold, because the memristors used to realize the BCM rules do not have tunable forgetting rates. In this work, the BCM rules with a tunable sliding frequency threshold are biorealistically implemented in SrTiO 3 ‐based second‐order memristors; the forgetting rate of the memristors is tuned by engineering the electrode/oxide interface through controlling the electrode composition. The approach of this work is precise and efficient, and the biorealistic implementation of the BCM rules in memristors improves the efficiency of the neural network for the artificial intelligent system.