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dc.contributor.authorBressloff, Paul C.
dc.date.accessioned2016-02-28T06:08:39Z
dc.date.available2016-02-28T06:08:39Z
dc.date.issued2010-01
dc.identifier.citationBressloff PC (2010) Stochastic Neural Field Theory and the System-Size Expansion. SIAM Journal on Applied Mathematics 70: 1488–1521. Available: http://dx.doi.org/10.1137/090756971.
dc.identifier.issn0036-1399
dc.identifier.issn1095-712X
dc.identifier.doi10.1137/090756971
dc.identifier.urihttp://hdl.handle.net/10754/599737
dc.description.abstractWe analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.
dc.description.sponsorshipReceived by the editors April 24, 2009; accepted for publication (in revised form) September 11, 2009; published electronically December 11, 2009. The work of the author was partially supported by award KUK-C1-013-4 made by King Abdullah University of Science and Technology (KAUST). http://www.siam.org/journals/siap/70-5/75697.html
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)
dc.subjectMaster equations
dc.subjectNeural field theory
dc.subjectPath integrals
dc.subjectStochastic processes
dc.subjectSystem-size expansion
dc.titleStochastic Neural Field Theory and the System-Size Expansion
dc.typeArticle
dc.identifier.journalSIAM Journal on Applied Mathematics
dc.contributor.institutionUniversity of Utah, Salt Lake City, United States
dc.contributor.institutionUniversity of Oxford, Oxford, United Kingdom
kaust.grant.numberKUK-C1-013-4


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