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Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity
Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5509735/ https://www.ncbi.nlm.nih.gov/pubmed/28706303 http://dx.doi.org/10.1038/s41598-017-05480-0 |
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author | Pedretti, G. Milo, V. Ambrogio, S. Carboni, R. Bianchi, S. Calderoni, A. Ramaswamy, N. Spinelli, A. S. Ielmini, D. |
author_facet | Pedretti, G. Milo, V. Ambrogio, S. Carboni, R. Bianchi, S. Calderoni, A. Ramaswamy, N. Spinelli, A. S. Ielmini, D. |
author_sort | Pedretti, G. |
collection | PubMed |
description | Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~10(4)) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks. |
format | Online Article Text |
id | pubmed-5509735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55097352017-07-17 Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity Pedretti, G. Milo, V. Ambrogio, S. Carboni, R. Bianchi, S. Calderoni, A. Ramaswamy, N. Spinelli, A. S. Ielmini, D. Sci Rep Article Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~10(4)) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks. Nature Publishing Group UK 2017-07-13 /pmc/articles/PMC5509735/ /pubmed/28706303 http://dx.doi.org/10.1038/s41598-017-05480-0 Text en © The Author(s) 2017 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 Pedretti, G. Milo, V. Ambrogio, S. Carboni, R. Bianchi, S. Calderoni, A. Ramaswamy, N. Spinelli, A. S. Ielmini, D. Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity |
title | Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity |
title_full | Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity |
title_fullStr | Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity |
title_full_unstemmed | Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity |
title_short | Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity |
title_sort | memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5509735/ https://www.ncbi.nlm.nih.gov/pubmed/28706303 http://dx.doi.org/10.1038/s41598-017-05480-0 |
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