On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex

In this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plast...

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Authors: Zamarreño Ramos, Carlos, Camuñas Mesa, Luis Alejandro, Pérez Carrasco, José Antonio, Masquelier, T., Serrano Gotarredona, María Teresa, Linares Barranco, Bernabé
Format: article
Status:Published version
Publication Date:2011
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/76768
Online Access:https://hdl.handle.net/11441/76768
https://doi.org/10.3389/fnins.2011.00026
Access Level:Open access
Keyword:STDP
Memristor
Synapses
Spikes
Nanotechnology
Visual cortex
Neural network
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spelling On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortexZamarreño Ramos, CarlosCamuñas Mesa, Luis AlejandroPérez Carrasco, José AntonioMasquelier, T.Serrano Gotarredona, María TeresaLinares Barranco, BernabéSTDPMemristorSynapsesSpikesNanotechnologyVisual cortexNeural networkIn this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on a behavioral macro-model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forward but also backward. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificialCMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices. All files used for the simulations are made available through the journal web site.European Union 216777Gobierno de España TEC2006-11730-C03-01, TEC2009-10639-C04-01Junta de Andalucía P06TIC01417Frontiers MediaArquitectura y Tecnología de ComputadoresTeoría de la Señal y Comunicaciones2011info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/76768https://doi.org/10.3389/fnins.2011.00026reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésFrontiers in Neuroscience, 5 (26), 1-22.216777TEC2006-11730-C03-01TEC2009-10639-C04-01P06TIC01417http://dx.doi.org/10.3389/fnins.2011.00026info:eu-repo/semantics/openAccessoai:idus.us.es:11441/767682026-06-17T12:51:07Z
dc.title.none.fl_str_mv On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
title On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
spellingShingle On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
Zamarreño Ramos, Carlos
STDP
Memristor
Synapses
Spikes
Nanotechnology
Visual cortex
Neural network
title_short On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
title_full On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
title_fullStr On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
title_full_unstemmed On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
title_sort On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
dc.creator.none.fl_str_mv Zamarreño Ramos, Carlos
Camuñas Mesa, Luis Alejandro
Pérez Carrasco, José Antonio
Masquelier, T.
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
author Zamarreño Ramos, Carlos
author_facet Zamarreño Ramos, Carlos
Camuñas Mesa, Luis Alejandro
Pérez Carrasco, José Antonio
Masquelier, T.
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
author_role author
author2 Camuñas Mesa, Luis Alejandro
Pérez Carrasco, José Antonio
Masquelier, T.
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
Teoría de la Señal y Comunicaciones
dc.subject.none.fl_str_mv STDP
Memristor
Synapses
Spikes
Nanotechnology
Visual cortex
Neural network
topic STDP
Memristor
Synapses
Spikes
Nanotechnology
Visual cortex
Neural network
description In this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on a behavioral macro-model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forward but also backward. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificialCMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices. All files used for the simulations are made available through the journal web site.
publishDate 2011
dc.date.none.fl_str_mv 2011
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/76768
https://doi.org/10.3389/fnins.2011.00026
url https://hdl.handle.net/11441/76768
https://doi.org/10.3389/fnins.2011.00026
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Frontiers in Neuroscience, 5 (26), 1-22.
216777
TEC2006-11730-C03-01
TEC2009-10639-C04-01
P06TIC01417
http://dx.doi.org/10.3389/fnins.2011.00026
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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