dc.contributor.author
Garagnani, Max
dc.contributor.author
Lucchese, Guglielmo
dc.contributor.author
Tomasello, Rosario
dc.contributor.author
Wennekers, Thomas
dc.contributor.author
Pulvermüller, Friedemann
dc.date.accessioned
2018-06-08T10:53:38Z
dc.date.available
2017-01-19T15:09:01.793Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/21288
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-24583
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.
en
dc.format.extent
19 Seiten
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
neural network
dc.subject
Hebbian learning
dc.subject.ddc
100 Philosophie und Psychologie::150 Psychologie
dc.title
A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain
Responses to Words and Pseudowords
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
Frontiers in Computational Neuroscience - 10 (2017), 145
dc.identifier.sepid
55213
dcterms.bibliographicCitation.doi
10.3389/fncom.2016.00145
dcterms.bibliographicCitation.url
http://dx.doi.org/10.3389/fncom.2016.00145
refubium.affiliation
Philosophie und Geisteswissenschaften
de
refubium.funding
Deutsche Forschungsgemeinschaft (DFG)
refubium.mycore.fudocsId
FUDOCS_document_000000026177
refubium.note.author
Gefördert durch die DFG und den Open-Access-Publikationsfonds der Freien
Universität Berlin.
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000007558
dcterms.accessRights.openaire
open access
dcterms.isPartOf.issn
1662-5188