Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron

Y. H. Liu, Xiao-Jing Wang

Research output: Contribution to journalArticle

Abstract

Although spike-frequency adaptation is a commonly observed property of neurons, its functional implications are still poorly understood. In this work, using a leaky integrate-and-fire neural model that includes a Ca2+-activated K+ current (IAHP), we develop a quantitative theory of adaptation temporal dynamics and compare our results with recent in vivo intracellular recordings from pyramidal cells in the cat visual cortex. Experimentally testable relations between the degree and the time constant of spike-frequency adaptation are predicted. We also contrast the IAHP model with an alternative adaptation model based on a dynamical firing threshold. Possible roles of adaptation in temporal computation are explored, as a time-delayed neuronal self-inhibition mechanism. Our results include the following: (1) given the same firing rate, the variability of interspike intervals (ISIs) is either reduced or enhanced by adaptation, depending on whether the IAHP dynamics is fast or slow compared with the mean ISI in the output spike train; (2) when the inputs are Poisson-distributed (uncorrelated), adaptation generates temporal anticorrelation between ISIs, we suggest that measurement of this negative correlation provides a probe to assess the strength of IAHP in vivo; (3) the forward masking effect produced by the slow dynamics of IAHP is nonlinear and effective at selecting the strongest input among competing sources of input signals.

Original languageEnglish (US)
Pages (from-to)25-45
Number of pages21
JournalJournal of Computational Neuroscience
Volume10
Issue number1
DOIs
StatePublished - 2001

Fingerprint

Neurons
Pyramidal Cells
Visual Cortex
Cats

Keywords

  • Calcium-activated potassium current
  • Correlation
  • Forward masking
  • Integrate-and-fire neuron
  • Spike-frequency adaptation
  • Variability

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. / Liu, Y. H.; Wang, Xiao-Jing.

In: Journal of Computational Neuroscience, Vol. 10, No. 1, 2001, p. 25-45.

Research output: Contribution to journalArticle

@article{2a7598798f204b13a942766f66f70b3a,
title = "Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron",
abstract = "Although spike-frequency adaptation is a commonly observed property of neurons, its functional implications are still poorly understood. In this work, using a leaky integrate-and-fire neural model that includes a Ca2+-activated K+ current (IAHP), we develop a quantitative theory of adaptation temporal dynamics and compare our results with recent in vivo intracellular recordings from pyramidal cells in the cat visual cortex. Experimentally testable relations between the degree and the time constant of spike-frequency adaptation are predicted. We also contrast the IAHP model with an alternative adaptation model based on a dynamical firing threshold. Possible roles of adaptation in temporal computation are explored, as a time-delayed neuronal self-inhibition mechanism. Our results include the following: (1) given the same firing rate, the variability of interspike intervals (ISIs) is either reduced or enhanced by adaptation, depending on whether the IAHP dynamics is fast or slow compared with the mean ISI in the output spike train; (2) when the inputs are Poisson-distributed (uncorrelated), adaptation generates temporal anticorrelation between ISIs, we suggest that measurement of this negative correlation provides a probe to assess the strength of IAHP in vivo; (3) the forward masking effect produced by the slow dynamics of IAHP is nonlinear and effective at selecting the strongest input among competing sources of input signals.",
keywords = "Calcium-activated potassium current, Correlation, Forward masking, Integrate-and-fire neuron, Spike-frequency adaptation, Variability",
author = "Liu, {Y. H.} and Xiao-Jing Wang",
year = "2001",
doi = "10.1023/A:1008916026143",
language = "English (US)",
volume = "10",
pages = "25--45",
journal = "Journal of Computational Neuroscience",
issn = "0929-5313",
publisher = "Springer Netherlands",
number = "1",

}

TY - JOUR

T1 - Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron

AU - Liu, Y. H.

AU - Wang, Xiao-Jing

PY - 2001

Y1 - 2001

N2 - Although spike-frequency adaptation is a commonly observed property of neurons, its functional implications are still poorly understood. In this work, using a leaky integrate-and-fire neural model that includes a Ca2+-activated K+ current (IAHP), we develop a quantitative theory of adaptation temporal dynamics and compare our results with recent in vivo intracellular recordings from pyramidal cells in the cat visual cortex. Experimentally testable relations between the degree and the time constant of spike-frequency adaptation are predicted. We also contrast the IAHP model with an alternative adaptation model based on a dynamical firing threshold. Possible roles of adaptation in temporal computation are explored, as a time-delayed neuronal self-inhibition mechanism. Our results include the following: (1) given the same firing rate, the variability of interspike intervals (ISIs) is either reduced or enhanced by adaptation, depending on whether the IAHP dynamics is fast or slow compared with the mean ISI in the output spike train; (2) when the inputs are Poisson-distributed (uncorrelated), adaptation generates temporal anticorrelation between ISIs, we suggest that measurement of this negative correlation provides a probe to assess the strength of IAHP in vivo; (3) the forward masking effect produced by the slow dynamics of IAHP is nonlinear and effective at selecting the strongest input among competing sources of input signals.

AB - Although spike-frequency adaptation is a commonly observed property of neurons, its functional implications are still poorly understood. In this work, using a leaky integrate-and-fire neural model that includes a Ca2+-activated K+ current (IAHP), we develop a quantitative theory of adaptation temporal dynamics and compare our results with recent in vivo intracellular recordings from pyramidal cells in the cat visual cortex. Experimentally testable relations between the degree and the time constant of spike-frequency adaptation are predicted. We also contrast the IAHP model with an alternative adaptation model based on a dynamical firing threshold. Possible roles of adaptation in temporal computation are explored, as a time-delayed neuronal self-inhibition mechanism. Our results include the following: (1) given the same firing rate, the variability of interspike intervals (ISIs) is either reduced or enhanced by adaptation, depending on whether the IAHP dynamics is fast or slow compared with the mean ISI in the output spike train; (2) when the inputs are Poisson-distributed (uncorrelated), adaptation generates temporal anticorrelation between ISIs, we suggest that measurement of this negative correlation provides a probe to assess the strength of IAHP in vivo; (3) the forward masking effect produced by the slow dynamics of IAHP is nonlinear and effective at selecting the strongest input among competing sources of input signals.

KW - Calcium-activated potassium current

KW - Correlation

KW - Forward masking

KW - Integrate-and-fire neuron

KW - Spike-frequency adaptation

KW - Variability

UR - http://www.scopus.com/inward/record.url?scp=0035081994&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035081994&partnerID=8YFLogxK

U2 - 10.1023/A:1008916026143

DO - 10.1023/A:1008916026143

M3 - Article

VL - 10

SP - 25

EP - 45

JO - Journal of Computational Neuroscience

JF - Journal of Computational Neuroscience

SN - 0929-5313

IS - 1

ER -