Details

Autor(en) / Beteiligte

• Xiong, Jue
• Yang, Rui
• Shaibo, Jamal
• Huang, He‐Ming
• He, Hui‐Kai
• Zhou, Wen
• Guo, Xin

Titel

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

Ist Teil von

• Advanced functional materials, 2019-02, Vol.29 (9), p.n/a

Ort / Verlag

Hoboken: Wiley Subscription Services, Inc

Erscheinungsjahr

2019

Quelle

Wiley

Beschreibungen/Notizen

• 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 SrTiO3‐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. Bienenstock, Cooper, and Munro (BCM) learning rules are the most accurate model of the synaptic plasticity and more compatible with spiking neural networks. The BCM rules with a tunable sliding frequency threshold are physically demonstrated in second‐order memristors with a tunable forgetting rate through interface engineering, which offers an efficient approach toward improving the efficiency of neural networks.