Cargando…
Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices
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, t...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083931/ https://www.ncbi.nlm.nih.gov/pubmed/32198368 http://dx.doi.org/10.1038/s41467-020-15158-3 |
_version_ | 1783508626432851968 |
---|---|
author | Wang, Zhongqiang Zeng, Tao Ren, Yanyun Lin, Ya Xu, Haiyang Zhao, Xiaoning Liu, Yichun Ielmini, Daniele |
author_facet | Wang, Zhongqiang Zeng, Tao Ren, Yanyun Lin, Ya Xu, Haiyang Zhao, Xiaoning Liu, Yichun Ielmini, Daniele |
author_sort | Wang, Zhongqiang |
collection | PubMed |
description | 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 WO(3−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. |
format | Online Article Text |
id | pubmed-7083931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70839312020-03-23 Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices Wang, Zhongqiang Zeng, Tao Ren, Yanyun Lin, Ya Xu, Haiyang Zhao, Xiaoning Liu, Yichun Ielmini, Daniele Nat Commun Article 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 WO(3−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. Nature Publishing Group UK 2020-03-20 /pmc/articles/PMC7083931/ /pubmed/32198368 http://dx.doi.org/10.1038/s41467-020-15158-3 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wang, Zhongqiang Zeng, Tao Ren, Yanyun Lin, Ya Xu, Haiyang Zhao, Xiaoning Liu, Yichun Ielmini, Daniele Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices |
title | Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices |
title_full | Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices |
title_fullStr | Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices |
title_full_unstemmed | Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices |
title_short | Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices |
title_sort | toward a generalized bienenstock-cooper-munro rule for spatiotemporal learning via triplet-stdp in memristive devices |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083931/ https://www.ncbi.nlm.nih.gov/pubmed/32198368 http://dx.doi.org/10.1038/s41467-020-15158-3 |
work_keys_str_mv | AT wangzhongqiang towardageneralizedbienenstockcoopermunroruleforspatiotemporallearningviatripletstdpinmemristivedevices AT zengtao towardageneralizedbienenstockcoopermunroruleforspatiotemporallearningviatripletstdpinmemristivedevices AT renyanyun towardageneralizedbienenstockcoopermunroruleforspatiotemporallearningviatripletstdpinmemristivedevices AT linya towardageneralizedbienenstockcoopermunroruleforspatiotemporallearningviatripletstdpinmemristivedevices AT xuhaiyang towardageneralizedbienenstockcoopermunroruleforspatiotemporallearningviatripletstdpinmemristivedevices AT zhaoxiaoning towardageneralizedbienenstockcoopermunroruleforspatiotemporallearningviatripletstdpinmemristivedevices AT liuyichun towardageneralizedbienenstockcoopermunroruleforspatiotemporallearningviatripletstdpinmemristivedevices AT ielminidaniele towardageneralizedbienenstockcoopermunroruleforspatiotemporallearningviatripletstdpinmemristivedevices |