Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

Bertrand Fontaine, Jose L. Pena, Romain Brette

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

Original languageEnglish (US)
Article numbere1003560
JournalPLoS Computational Biology
Volume10
Issue number4
DOIs
StatePublished - 2014

Fingerprint

Membrane Potential
Spike
membrane potential
Membrane Potentials
Neurons
neurons
membrane
Membranes
Neuron
Strigiformes
Tyto alba
Noise
Time Scales
timescale
Electric potential
Voltage
Fluctuations

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Modeling and Simulation
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Molecular Biology
  • Ecology
  • Cellular and Molecular Neuroscience
  • Medicine(all)

Cite this

Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo. / Fontaine, Bertrand; Pena, Jose L.; Brette, Romain.

In: PLoS Computational Biology, Vol. 10, No. 4, e1003560, 2014.

Research output: Contribution to journalArticle

@article{77f4a12b1224413ea18b7b746ba7984e,
title = "Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo",
abstract = "Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.",
author = "Bertrand Fontaine and Pena, {Jose L.} and Romain Brette",
year = "2014",
doi = "10.1371/journal.pcbi.1003560",
language = "English (US)",
volume = "10",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "4",

}

TY - JOUR

T1 - Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

AU - Fontaine, Bertrand

AU - Pena, Jose L.

AU - Brette, Romain

PY - 2014

Y1 - 2014

N2 - Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

AB - Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

UR - http://www.scopus.com/inward/record.url?scp=84901293963&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901293963&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1003560

DO - 10.1371/journal.pcbi.1003560

M3 - Article

VL - 10

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 4

M1 - e1003560

ER -