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Spatially localized cluster solutions in inhibitory neural networks

dc.contributor.authorRyu, Hwayeon
dc.contributor.authorMiller, Jennifer
dc.contributor.authorTeymuroglu, Zeynep
dc.contributor.authorWang, Xueying
dc.contributor.authorBooth, Victoria
dc.contributor.authorCampbell, Sue Ann
dc.date.accessioned2022-04-14T18:02:25Z
dc.date.available2022-04-14T18:02:25Z
dc.date.embargountil2022-06-14
dc.date.issued2021-06
dc.descriptionThe final publication is available at Elsevier via http://dx.doi.org/10.1016/j.mbs.2021.108591. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.description.abstractNeurons in the inhibitory network of the striatum display cell assembly firing patterns which recent results suggest may consist of spatially compact neural clusters. Previous computational modeling of striatal neural networks has indicated that non-monotonic, distance-dependent coupling may promote spatially localized cluster firing. Here, we identify conditions for the existence and stability of cluster firing solutions in which clusters consist of spatially adjacent neurons in inhibitory neural networks. We consider simple non-monotonic, distance-dependent connectivity schemes in weakly coupled 1-D networks where cells make stronger connections with their th nearest neighbors on each side and weaker connections with closer neighbors. Using the phase model reduction of the network system, we prove the existence of cluster solutions where neurons that are spatially close together are also synchronized in the same cluster, and find stability conditions for these solutions. Our analysis predicts the long-term behavior for networks of neurons, and we confirm our results by numerical simulations of biophysical neuron network models. Our results demonstrate that an inhibitory network with non-monotonic, distance-dependent connectivity can exhibit cluster solutions where adjacent cells fire together.en
dc.description.sponsorshipAmerican Institute of Mathematics, Structured Quartet Research Ensembles program || Natural Sciences and Engineering Research Council of Canada.en
dc.identifier.urihttps://doi.org/10.1016/j.mbs.2021.108591
dc.identifier.urihttp://hdl.handle.net/10012/18148
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofseriesMathematical Biosciences;
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectinhibitory networksen
dc.subjectconnectivityen
dc.subjectclustersen
dc.subjectphase modelen
dc.titleSpatially localized cluster solutions in inhibitory neural networksen
dc.typeArticleen
dcterms.bibliographicCitationRyu, H., Miller, J., Teymuroglu, Z., Wang, X., Booth, V., & Campbell, S. A. (2021). Spatially localized cluster solutions in inhibitory neural networks. Mathematical Biosciences, 336, 108591. https://doi.org/10.1016/j.mbs.2021.108591en
uws.contributor.affiliation1Faculty of Mathematicsen
uws.contributor.affiliation2Applied Mathematicsen
uws.contributor.affiliation2Centre for Theoretical Neuroscience (CTN)en
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen
uws.typeOfResourceTexten

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