Stochastic transitions into silence cause noise correlations in cortical circuits

Gabriela Mochol, Ainhoa Hermoso-Mendizabal, Shuzo Sakata, Kenneth D. Harris, Jaime de la Rocha

Research output: Contribution to journalArticle

  • 25 Citations

Abstract

The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.

LanguageEnglish
Pages3529–3534
Number of pages6
JournalProceedings of the National Academy of Sciences
Volume112
Issue number11
Early online date4 Mar 2015
DOIs
Publication statusPublished - 2015

Fingerprint

Noise
Neurons
Brain
Auditory Cortex
Population

Keywords

  • neuronal variability
  • noise correlations
  • brain state
  • auditory cortex
  • stochastic network dynamics

Cite this

Mochol, Gabriela ; Hermoso-Mendizabal, Ainhoa ; Sakata, Shuzo ; Harris, Kenneth D. ; de la Rocha, Jaime. / Stochastic transitions into silence cause noise correlations in cortical circuits. In: Proceedings of the National Academy of Sciences . 2015 ; Vol. 112, No. 11. pp. 3529–3534.
@article{47004ef3ddc942ac9a11dbfe433bab35,
title = "Stochastic transitions into silence cause noise correlations in cortical circuits",
abstract = "The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.",
keywords = "neuronal variability, noise correlations, brain state, auditory cortex, stochastic network dynamics",
author = "Gabriela Mochol and Ainhoa Hermoso-Mendizabal and Shuzo Sakata and Harris, {Kenneth D.} and {de la Rocha}, Jaime",
year = "2015",
doi = "10.1073/pnas.1410509112",
language = "English",
volume = "112",
pages = "3529–3534",
journal = "Proceedings of the National Academy of Sciences",
issn = "1091-6490",
number = "11",

}

Stochastic transitions into silence cause noise correlations in cortical circuits. / Mochol, Gabriela; Hermoso-Mendizabal, Ainhoa; Sakata, Shuzo; Harris, Kenneth D.; de la Rocha, Jaime.

In: Proceedings of the National Academy of Sciences , Vol. 112, No. 11, 2015, p. 3529–3534.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Stochastic transitions into silence cause noise correlations in cortical circuits

AU - Mochol, Gabriela

AU - Hermoso-Mendizabal, Ainhoa

AU - Sakata, Shuzo

AU - Harris, Kenneth D.

AU - de la Rocha, Jaime

PY - 2015

Y1 - 2015

N2 - The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.

AB - The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.

KW - neuronal variability

KW - noise correlations

KW - brain state

KW - auditory cortex

KW - stochastic network dynamics

U2 - 10.1073/pnas.1410509112

DO - 10.1073/pnas.1410509112

M3 - Article

VL - 112

SP - 3529

EP - 3534

JO - Proceedings of the National Academy of Sciences

T2 - Proceedings of the National Academy of Sciences

JF - Proceedings of the National Academy of Sciences

SN - 1091-6490

IS - 11

ER -