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Bienenstock, Cooper, and Munro Learning Rules Realized in Second‐Order Memristors with Tunable Forgetting Rate
Author(s) -
Xiong Jue,
Yang Rui,
Shaibo Jamal,
Huang HeMing,
He HuiKai,
Zhou Wen,
Guo Xin
Publication year - 2019
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.201807316
Subject(s) - forgetting , memristor , artificial neural network , realization (probability) , neuromorphic engineering , computer science , materials science , artificial intelligence , electronic engineering , engineering , mathematics , philosophy , linguistics , statistics
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.