Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices

Zhongqiang Wang, Tao Zeng, Yanyun Ren, Ya Lin, Haiyang Xu (), Xiaoning Zhao, Yichun Liu () and Daniele Ielmini ()
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Zhongqiang Wang: Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education
Tao Zeng: Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education
Yanyun Ren: Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education
Ya Lin: Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education
Haiyang Xu: Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education
Xiaoning Zhao: Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education
Yichun Liu: Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education
Daniele Ielmini: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timing-dependent plasticity scheme in a WO3−x memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a history-dependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks.

Date: 2020
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DOI: 10.1038/s41467-020-15158-3

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