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dc.contributor.author
Galluppi, Francesco
dc.contributor.author
Lagorce, Xavier
dc.contributor.author
Stromatias, Evangelos
dc.contributor.author
Pfeiffer, Michael
dc.contributor.author
Plana, Luis A.
dc.contributor.author
Furber, Steve B.
dc.contributor.author
Benosman, Ryad B.
dc.date.accessioned
2019-05-29T11:54:27Z
dc.date.available
2017-06-11T15:51:18Z
dc.date.available
2019-05-29T11:54:27Z
dc.date.issued
2015-01-20
dc.identifier.issn
1662-453X
dc.identifier.issn
1662-4548
dc.identifier.other
10.3389/fnins.2014.00429
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/97354
dc.identifier.doi
10.3929/ethz-b-000097354
dc.description.abstract
Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
SpiNNaker
en_US
dc.subject
Learning
en_US
dc.subject
Plasticity
en_US
dc.subject
Neuromorphic hardware
en_US
dc.subject
STDP
en_US
dc.subject
BCM
en_US
dc.title
A framework for plasticity implementation on the SpiNNaker neural architecture
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Frontiers in Neuroscience
ethz.journal.volume
8
en_US
ethz.journal.abbreviated
Front Neurosci
ethz.pages.start
429
en_US
ethz.size
20 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
009497874
ethz.publication.place
Lausane
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
03453 - Douglas, Rodney J.
en_US
ethz.leitzahl.certified
03453 - Douglas, Rodney J.
ethz.date.deposited
2017-06-11T15:51:26Z
ethz.source
ECIT
ethz.identifier.importid
imp593652e02bd9598791
ethz.ecitpid
pub:152293
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-20T14:16:29Z
ethz.rosetta.lastUpdated
2019-05-29T11:54:57Z
ethz.rosetta.versionExported
true
ethz.COinS
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