Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations

dc.contributor.authorDonner, Christian
dc.contributor.authorObermayer, Klaus
dc.contributor.authorShimazaki, Hideaki
dc.date.accessioned2020-11-26T17:00:43Z
dc.date.available2020-11-26T17:00:43Z
dc.date.issued2017-01-17
dc.description.abstractThe models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons. Here by introducing multiple analytic approximation methods to a state-space model of neural population activity, we make it possible to estimate dynamic pairwise interactions of up to 60 neurons. More specifically, we applied the pseudolikelihood approximation to the state-space model, and combined it with the Bethe or TAP mean-field approximation to make the sequential Bayesian estimation of the model parameters possible. The large-scale analysis allows us to investigate dynamics of macroscopic properties of neural circuitries underlying stimulus processing and behavior. We show that the model accurately estimates dynamics of network properties such as sparseness, entropy, and heat capacity by simulated data, and demonstrate utilities of these measures by analyzing activity of monkey V4 neurons as well as a simulated balanced network of spiking neurons.en
dc.description.sponsorshipDFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systemenen
dc.identifier.eissn1553-7358
dc.identifier.issn1553-734X
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12070
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-10944
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc610 Medizin und Gesundheiten
dc.subject.otherneural populationen
dc.subject.otherneuronen
dc.subject.otherapproximation methoden
dc.subject.otherneural networken
dc.titleApproximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populationsen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumbere1005309
dcterms.bibliographicCitation.doi10.1371/journal.pcbi.1005309
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitlePLOS Computational Biologyen
dcterms.bibliographicCitation.originalpublishernamePublic Library of Science (PLoS)en
dcterms.bibliographicCitation.originalpublisherplaceSan Francisco, Calif.en
dcterms.bibliographicCitation.volume13
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Softwaretechnik und Theoretische Informatik>FG Neuronale Informationsverarbeitungde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Neuronale Informationsverarbeitungde
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
Files
Original bundle
Now showing 1 - 1 of 1
Loading…
Thumbnail Image
Name:
journal.pcbi.1005309.pdf
Size:
3.45 MB
Format:
Adobe Portable Document Format
Description:
Collections