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dc.contributor.authorMeng, L.en_US
dc.contributor.authorKramer, M. A.en_US
dc.contributor.authorMiddleton, S. J.en_US
dc.contributor.authorWhittington, M. A.en_US
dc.contributor.authorEden, U. T.en_US
dc.date.accessioned2019-03-04T18:35:18Z
dc.date.available2019-03-04T18:35:18Z
dc.date.issued2014
dc.identifier.citationL Meng, MA Kramer, SJ Middleton, MA Whittington, UT Eden. 2014. "A unified approach to linking experimental, statistical and computational analysis of spike train data." PloS one, Volume 9, Issue 1, pp. e85269 - e85269. https://doi.org/10.1371/journal.pone.0085269
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/2144/33700
dc.description.abstractA fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.en_US
dc.format.extentp. e85269 - e85269en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofPloS one
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & technologyen_US
dc.subjectMultidisciplinary sciencesen_US
dc.subjectSingle-neuron modelsen_US
dc.subjectSpectrumen_US
dc.subjectConstructionen_US
dc.subjectExcitabilityen_US
dc.subjectConductanceen_US
dc.subjectCurrentsen_US
dc.subjectAction potentialsen_US
dc.subjectAnimalsen_US
dc.subjectBiophysical phenomenaen_US
dc.subjectComputer simulationen_US
dc.subjectMaleen_US
dc.subjectModels, neurologicalen_US
dc.subjectRatsen_US
dc.subjectRats, Wistaren_US
dc.subjectStatistics as topicen_US
dc.subjectAction potentialsen_US
dc.subjectMD multidisciplinaryen_US
dc.subjectGeneral science & technologyen_US
dc.titleA unified approach to linking experimental, statistical and computational analysis of spike train dataen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1371/journal.pone.0085269
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Mathematics & Statisticsen_US
pubs.publication-statusPublisheden_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International