Finding the event structure of neuronal spike trains

J. Vincent Toups, Jean-Marc Fellous, Peter J. Thomas, Terrence J. Sejnowski, Paul H. Tiesinga

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)2169-2208
Number of pages40
JournalNeural Computation
Volume23
Issue number9
DOIs
StatePublished - Sep 2011

Fingerprint

Neurons
Sensory Receptor Cells
Information Systems
Injections
Event Structures
Train
Neuron
Brain
In Vitro Techniques

ASJC Scopus subject areas

  • Cognitive Neuroscience

Cite this

Vincent Toups, J., Fellous, J-M., Thomas, P. J., Sejnowski, T. J., & Tiesinga, P. H. (2011). Finding the event structure of neuronal spike trains. Neural Computation, 23(9), 2169-2208. https://doi.org/10.1162/NECO_a_00173

Finding the event structure of neuronal spike trains. / Vincent Toups, J.; Fellous, Jean-Marc; Thomas, Peter J.; Sejnowski, Terrence J.; Tiesinga, Paul H.

In: Neural Computation, Vol. 23, No. 9, 09.2011, p. 2169-2208.

Research output: Contribution to journalArticle

Vincent Toups, J, Fellous, J-M, Thomas, PJ, Sejnowski, TJ & Tiesinga, PH 2011, 'Finding the event structure of neuronal spike trains', Neural Computation, vol. 23, no. 9, pp. 2169-2208. https://doi.org/10.1162/NECO_a_00173
Vincent Toups J, Fellous J-M, Thomas PJ, Sejnowski TJ, Tiesinga PH. Finding the event structure of neuronal spike trains. Neural Computation. 2011 Sep;23(9):2169-2208. https://doi.org/10.1162/NECO_a_00173
Vincent Toups, J. ; Fellous, Jean-Marc ; Thomas, Peter J. ; Sejnowski, Terrence J. ; Tiesinga, Paul H. / Finding the event structure of neuronal spike trains. In: Neural Computation. 2011 ; Vol. 23, No. 9. pp. 2169-2208.
@article{3839806c889048799d81bb04475b94eb,
title = "Finding the event structure of neuronal spike trains",
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.",
author = "{Vincent Toups}, J. and Jean-Marc Fellous and Thomas, {Peter J.} and Sejnowski, {Terrence J.} and Tiesinga, {Paul H.}",
year = "2011",
month = "9",
doi = "10.1162/NECO_a_00173",
language = "English (US)",
volume = "23",
pages = "2169--2208",
journal = "Neural Computation",
issn = "0899-7667",
publisher = "MIT Press Journals",
number = "9",

}

TY - JOUR

T1 - Finding the event structure of neuronal spike trains

AU - Vincent Toups, J.

AU - Fellous, Jean-Marc

AU - Thomas, Peter J.

AU - Sejnowski, Terrence J.

AU - Tiesinga, Paul H.

PY - 2011/9

Y1 - 2011/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80051601956&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051601956&partnerID=8YFLogxK

U2 - 10.1162/NECO_a_00173

DO - 10.1162/NECO_a_00173

M3 - Article

VL - 23

SP - 2169

EP - 2208

JO - Neural Computation

JF - Neural Computation

SN - 0899-7667

IS - 9

ER -