Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network

DC FieldValueLanguage
dc.contributor.authorFinger, Holger
dc.contributor.authorKoenig, Peter
dc.date.accessioned2021-12-23T16:13:41Z-
dc.date.available2021-12-23T16:13:41Z-
dc.date.issued2014
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/10694-
dc.description.abstractSynchronization has been suggested as a mechanism of binding distributed feature representations facilitating segmentation of visual stimuli. Here we investigate this concept based on unsupervised learning using natural visual stimuli. We simulate dual-variable neural oscillators with separate activation and phase variables. The binding of a set of neurons is coded by synchronized phase variables. The network of tangential synchronizing connections learned from the induced activations exhibits small-world properties and allows binding even over larger distances. We evaluate the resulting dynamic phase maps using segmentation masks labeled by human experts. Our simulation results show a continuously increasing phase synchrony between neurons within the labeled segmentation masks. The evaluation of the network dynamics shows that the synchrony between network nodes establishes a relational coding of the natural image inputs. This demonstrates that the concept of binding by synchrony is applicable in the context of unsupervised learning using natural visual stimuli.
dc.description.sponsorshipDFGGerman Research Foundation (DFG)European Commission [SFB 936]; This work was funded by the DFG through SFB 936 Multi-Site Communication in the Brain.
dc.language.isoen
dc.publisherFRONTIERS MEDIA SA
dc.relation.ispartofFRONTIERS IN COMPUTATIONAL NEUROSCIENCE
dc.subjectbinding
dc.subjectCODE
dc.subjectCOMPUTATIONS
dc.subjectMathematical & Computational Biology
dc.subjectMODEL
dc.subjectnatural image statistics
dc.subjectNeurosciences
dc.subjectNeurosciences & Neurology
dc.subjectnormative model
dc.subjectobject label
dc.subjectoscillation
dc.subjectRESPONSES
dc.subjectscene segmentation
dc.subjectSMALL-WORLD
dc.subjectSTATISTICS
dc.subjectsynchronization
dc.subjectunsupervised learning
dc.subjectVISUAL-CORTEX
dc.titlePhase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network
dc.typejournal article
dc.identifier.doi10.3389/fncom.2013.00195
dc.identifier.isiISI:000332458600001
dc.description.volume7
dc.contributor.orcid0000-0003-3654-5267
dc.contributor.researcheridABB-2380-2020
dc.identifier.eissn16625188
dc.publisher.placeAVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
dcterms.isPartOf.abbreviationFront. Comput. Neurosci.
dcterms.oaStatusGreen Published, gold
crisitem.author.deptInstitut für Kognitionswissenschaft-
crisitem.author.deptFB 05 - Biologie/Chemie-
crisitem.author.deptidinstitute28-
crisitem.author.deptidfb05-
crisitem.author.orcid0000-0003-3654-5267-
crisitem.author.parentorgFB 08 - Humanwissenschaften-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.grandparentorgUniversität Osnabrück-
crisitem.author.netidKoPe298-
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