Optimal control of a Wilson–Cowan model of neural population dynamics

dc.contributor.authorSalfenmoser, Lena
dc.contributor.authorObermayer, Klaus
dc.date.accessioned2023-06-07T12:51:54Z
dc.date.available2023-06-07T12:51:54Z
dc.date.issued2023-04-19
dc.description.abstractNonlinear dynamical systems describe neural activity at various scales and are frequently used to study brain functions and the impact of external perturbations. Here, we explore methods from optimal control theory (OCT) to study efficient, stimulating “control” signals designed to make the neural activity match desired targets. Efficiency is quantified by a cost functional, which trades control strength against closeness to the target activity. Pontryagin’s principle then enables to compute the cost-minimizing control signal. We then apply OCT to a Wilson–Cowan model of coupled excitatory and inhibitory neural populations. The model exhibits an oscillatory regime, low- and high-activity fixed points, and a bistable regime where low- and high-activity states coexist. We compute an optimal control for a state-switching (bistable regime) and a phase-shifting task (oscillatory regime) and allow for a finite transition period before penalizing the deviation from the target state. For the state-switching task, pulses of limited input strength push the activity minimally into the target basin of attraction. Pulse shapes do not change qualitatively when varying the duration of the transition period. For the phase-shifting task, periodic control signals cover the whole transition period. Amplitudes decrease when transition periods are extended, and their shapes are related to the phase sensitivity profile of the model to pulsed perturbations. Penalizing control strength via the integrated 1-norm yields control inputs targeting only one population for both tasks. Whether control inputs drive the excitatory or inhibitory population depends on the state-space location.en
dc.description.sponsorshipDFG, 163436311, SFB 910: Control of Self-Organising Non-Linear Systems: Theoretical Methods and Concepts of Application
dc.identifier.eissn1089-7682
dc.identifier.issn1054-1500
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/19131
dc.identifier.urihttps://doi.org/10.14279/depositonce-17928
dc.language.isoen
dc.relation.references10.14279/depositonce-17170
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc500 Naturwissenschaften und Mathematik::510 Mathematik::518 Numerische Analysis
dc.subject.otherbistabilityen
dc.subject.othernonlinear control theoryen
dc.subject.othernonlinear systemsen
dc.subject.otheroptimization algorithmsen
dc.subject.otherneural oscillationsen
dc.subject.otherbrain stimulationen
dc.titleOptimal control of a Wilson–Cowan model of neural population dynamicsen
dc.title.translatedOptimale Kontrolle eines Wilson-Cowan-Modells der neuronalen Populationsdynamikde
dc.typeArticle
dc.type.versionacceptedVersion
dcterms.bibliographicCitation.articlenumber043135
dcterms.bibliographicCitation.doi10.1063/5.0144682
dcterms.bibliographicCitation.issue4
dcterms.bibliographicCitation.journaltitleChaos: An Interdisciplinary Journal of Nonlinear Science
dcterms.bibliographicCitation.originalpublishernameAmerican Institute of Physics
dcterms.bibliographicCitation.originalpublisherplaceWoodbury, NY
dcterms.bibliographicCitation.volume33
dcterms.rightsHolder.noteThis article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in: Salfenmoser, Lena; Obermayer, Klaus (2023). Optimal control of a Wilson–Cowan model of neural population dynamics. Chaos, 33(4), 043135. https://doi.org/10.1063/5.0144682. and may be found at https://doi.org/10.1063/5.0144682.
dcterms.rightsHolder.referenceVerlagspolicy
dcterms.rightsHolder.urlhttps://web.archive.org/web/20230318152642/https://publishing.aip.org/resources/researchers/rights-and-permissions/sharing-content-online/
tub.accessrights.dnbfree
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Neuronale Informationsverarbeitung
tub.publisher.universityorinstitutionTechnische Universität Berlin

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