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dc.contributor.authorGaragnani, M
dc.contributor.authorLucchese, G
dc.contributor.authorTomasello, R
dc.contributor.authorWennekers, Thomas
dc.contributor.authorPulvermüller, F
dc.date.accessioned2017-02-13T15:04:31Z
dc.date.accessioned2017-08-03T11:32:54Z
dc.date.available2017-02-13T15:04:31Z
dc.date.available2017-08-03T11:32:54Z
dc.date.issued2017-01-18
dc.identifier.issn1662-5188
dc.identifier.issn1662-5188
dc.identifier.otherARTN 145
dc.identifier.urihttp://hdl.handle.net/10026.1/9662
dc.description.abstract

Experimental evidence indicates that neurophysiological responses to well-known meaningful sensory items and symbols (such as familiar objects, faces, or words) differ from those to matched but novel and senseless materials (unknown objects, scrambled faces, and pseudowords). Spectral responses in the high beta- and gamma-band have been observed to be generally stronger to familiar stimuli than to unfamiliar ones. These differences have been hypothesized to be caused by the activation of distributed neuronal circuits or cell assemblies, which act as long-term memory traces for learned familiar items only. Here, we simulated word learning using a biologically constrained neurocomputational model of the left-hemispheric cortical areas known to be relevant for language and conceptual processing. The 12-area spiking neural-network architecture implemented replicates physiological and connectivity features of primary, secondary, and higher-association cortices in the frontal, temporal, and occipital lobes of the human brain. We simulated elementary aspects of word learning in it, focussing specifically on semantic grounding in action and perception. As a result of spike-driven Hebbian synaptic plasticity mechanisms, distributed, stimulus-specific cell-assembly (CA) circuits spontaneously emerged in the network. After training, presentation of one of the learned "word" forms to the model correlate of primary auditory cortex induced periodic bursts of activity within the corresponding CA, leading to oscillatory phenomena in the entire network and spontaneous across-area neural synchronization. Crucially, Morlet wavelet analysis of the network's responses recorded during presentation of learned meaningful "word" and novel, senseless "pseudoword" patterns revealed stronger induced spectral power in the gamma-band for the former than the latter, closely mirroring differences found in neurophysiological data. Furthermore, coherence analysis of the simulated responses uncovered dissociated category specific patterns of synchronous oscillations in distant cortical areas, including indirectly connected primary sensorimotor areas. Bridging the gap between cellular-level mechanisms, neuronal-population behavior, and cognitive function, the present model constitutes the first spiking, neurobiologically, and anatomically realistic model able to explain high-frequency oscillatory phenomena indexing language processing on the basis of dynamics and competitive interactions of distributed cell-assembly circuits which emerge in the brain as a result of Hebbian learning and sensorimotor experience.

dc.format.extent145-
dc.format.mediumElectronic-eCollection
dc.languageeng
dc.language.isoen
dc.publisherFrontiers Media SA
dc.relation.replaceshttp://hdl.handle.net/10026.1/8462
dc.relation.replaces10026.1/8462
dc.subjectneural network
dc.subjectcell assembly
dc.subjectgamma band
dc.subjectlanguage
dc.subjectsynchrony
dc.subjectsimulation
dc.subjectHebbian learning
dc.titleA Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000392334900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume10
plymouth.publication-statusPublished
plymouth.journalFrontiers in Computational Neuroscience
dc.identifier.doi10.3389/fncom.2016.00145
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Admin Group - REF
plymouth.organisational-group/Plymouth/Admin Group - REF/REF Admin Group - FoSE
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeSwitzerland
dcterms.dateAccepted2016-12-26
dc.identifier.eissn1662-5188
dc.rights.embargoperiodNo embargo
rioxxterms.funderEPSRC
rioxxterms.identifier.projectBABEL
rioxxterms.versionVersion of Record
rioxxterms.versionofrecord10.3389/fncom.2016.00145
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-01-18
rioxxterms.typeJournal Article/Review
plymouth.funderBABEL::EPSRC
plymouth.oa-locationhttp://journal.frontiersin.org/article/10.3389/fncom.2016.00145/full


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