Finding The Event Structure Of Neuronal Spike Trains

Title:
Finding The Event Structure Of Neuronal Spike Trains
Authors:
Toups, J. Vincent; Fellous, Jean-Marc; Thomas, Peter J.; Sejnowski, Terrence J.; Tiesinga, Paul H.
Abstract:
Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.
Citation:
Toups, J. Vincent, Jean-Marc Fellous, Peter J. Thomas, Terrence J. Sejnowski, et al. 2011. "Finding The Event Structure Of Neuronal Spike Trains." Neural Computation 23(9): 2169-2208.
Publisher:
Massachusetts Institute of Technology Press
DATE ISSUED:
2011-09
Department:
Mathematics
Type:
article
PUBLISHED VERSION:
10.1162/NECO_a_00173
PERMANENT LINK:
http://hdl.handle.net/11282/310083

Full metadata record

DC FieldValue Language
dc.contributor.authorToups, J. Vincenten_US
dc.contributor.authorFellous, Jean-Marcen_US
dc.contributor.authorThomas, Peter J.en_US
dc.contributor.authorSejnowski, Terrence J.en_US
dc.contributor.authorTiesinga, Paul H.en_US
dc.date.accessioned2013-12-23T16:24:53Z-
dc.date.available2013-12-23T16:24:53Z-
dc.date.issued2011-09en
dc.identifier.citationToups, J. Vincent, Jean-Marc Fellous, Peter J. Thomas, Terrence J. Sejnowski, et al. 2011. "Finding The Event Structure Of Neuronal Spike Trains." Neural Computation 23(9): 2169-2208.en_US
dc.identifier.issn0899-7667en_US
dc.identifier.urihttp://hdl.handle.net/11282/310083-
dc.description.abstractNeurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.en_US
dc.publisherMassachusetts Institute of Technology Pressen_US
dc.identifier.doi10.1162/NECO_a_00173-
dc.subject.departmentMathematicsen_US
dc.titleFinding The Event Structure Of Neuronal Spike Trainsen_US
dc.typearticleen_US
dc.identifier.journalNeural Computationen_US
dc.identifier.volume23en_US
dc.identifier.issue9en_US
dc.identifier.startpage2169en_US
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