Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity

Abstract 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 t...

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Autores principales:	G. Pedretti, V. Milo, S. Ambrogio, R. Carboni, S. Bianchi, A. Calderoni, N. Ramaswamy, A. S. Spinelli, D. Ielmini
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1bedd02d99f6453c9fdb140384853231
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spelling	oai:doaj.org-article:1bedd02d99f6453c9fdb1403848532312021-12-02T16:06:47ZMemristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity10.1038/s41598-017-05480-02045-2322https://doaj.org/article/1bedd02d99f6453c9fdb1403848532312017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05480-0https://doaj.org/toc/2045-2322Abstract 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 (~104) 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.G. PedrettiV. MiloS. AmbrogioR. CarboniS. BianchiA. CalderoniN. RamaswamyA. S. SpinelliD. IelminiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q G. Pedretti V. Milo S. Ambrogio R. Carboni S. Bianchi A. Calderoni N. Ramaswamy A. S. Spinelli D. Ielmini Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity
description	Abstract 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 (~104) 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	article
author	G. Pedretti V. Milo S. Ambrogio R. Carboni S. Bianchi A. Calderoni N. Ramaswamy A. S. Spinelli D. Ielmini
author_facet	G. Pedretti V. Milo S. Ambrogio R. Carboni S. Bianchi A. Calderoni N. Ramaswamy A. S. Spinelli D. Ielmini
author_sort	G. Pedretti
title	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_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_sort	memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/1bedd02d99f6453c9fdb140384853231
work_keys_str_mv	AT gpedretti memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity AT vmilo memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity AT sambrogio memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity AT rcarboni memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity AT sbianchi memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity AT acalderoni memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity AT nramaswamy memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity AT asspinelli memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity AT dielmini memristiveneuralnetworkforonlinelearningandtrackingwithbraininspiredspiketimingdependentplasticity
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Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity

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