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dc.contributor.author
Coath, Martin
dc.contributor.author
Sheik, Sadique
dc.contributor.author
Chicca, Elisabetta
dc.contributor.author
Indiveri, Giacomo
dc.contributor.author
Denham, Susan L.
dc.contributor.author
Wennekers, Thomas
dc.date.accessioned
2019-04-04T15:55:14Z
dc.date.available
2017-06-11T15:19:59Z
dc.date.available
2019-04-04T15:55:14Z
dc.date.issued
2014-01-17
dc.identifier.issn
1662-453X
dc.identifier.issn
1662-4548
dc.identifier.other
10.3389/fnins.2013.00278
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/96005
dc.identifier.doi
10.3929/ethz-b-000096005
dc.description.abstract
We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to “noisy” stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds.
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/3.0/
dc.subject
Auditory
en_US
dc.subject
Modeling
en_US
dc.subject
Plasticity
en_US
dc.subject
Information
en_US
dc.subject
VLSI
en_US
dc.subject
Neurommphic
en_US
dc.title
A robust sound perception model suitable for neuromorphic implementation
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
ethz.journal.title
Frontiers in Neuroscience
ethz.journal.volume
7
en_US
ethz.journal.abbreviated
Front Neurosci
ethz.pages.start
278
en_US
ethz.size
10 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
009497874
ethz.publication.place
Lausanne
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
2017-06-11T15:20:45Z
ethz.source
ECIT
ethz.identifier.importid
imp593652c93b72b76489
ethz.ecitpid
pub:150568
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-13T03:30:52Z
ethz.rosetta.lastUpdated
2019-04-04T15:55:20Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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