Architectural and synaptic mechanisms underlying coherent spontaneous activity in V1

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Abstract

To investigate the existence and the characteristics of possible cortical operating points of the primary visual cortex, as manifested by the coherent spontaneous ongoing activity revealed by real-time optical imaging based on voltage-sensitive dyes, we studied numerically a very large-scale (≈5 × 105) conductance-based, integrate-and-fire neuronal network model of an ≈16-mm2 patch of 64 orientation hypercolumns, which incorporates both isotropic local couplings and lateral orientation-specific long-range connections with a slow NMDA component. A dynamic scenario of an intermittent desuppressed state (IDS) is identified in the computational model, which is a dynamic state of (i) high conductance, (ii) strong inhibition, and (iii) large fluctuations that arise from intermittent spiking events that are strongly correlated in time as well as in orientation domains, with the correlation time of the fluctuations controlled by the NMDA decay time scale. Our simulation results demonstrate that the IDS state captures numerically many aspects of experimental observation related to spontaneous ongoing activity, and the specific network mechanism of the IDS may suggest cortical mechanisms and the cortical operating point underlying observed spontaneous activity.

Original languageEnglish (US)
Pages (from-to)5868-5873
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume102
Issue number16
DOIs
StatePublished - Apr 19 2005

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N-Methylaspartate
Optical Imaging
Visual Cortex
Coloring Agents
Observation

Keywords

  • Fluctuation
  • Horizontal connection
  • Neuronal networks
  • Optical imaging
  • Spatiotemporal patterns

ASJC Scopus subject areas

  • Genetics
  • General

Cite this

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title = "Architectural and synaptic mechanisms underlying coherent spontaneous activity in V1",
abstract = "To investigate the existence and the characteristics of possible cortical operating points of the primary visual cortex, as manifested by the coherent spontaneous ongoing activity revealed by real-time optical imaging based on voltage-sensitive dyes, we studied numerically a very large-scale (≈5 × 105) conductance-based, integrate-and-fire neuronal network model of an ≈16-mm2 patch of 64 orientation hypercolumns, which incorporates both isotropic local couplings and lateral orientation-specific long-range connections with a slow NMDA component. A dynamic scenario of an intermittent desuppressed state (IDS) is identified in the computational model, which is a dynamic state of (i) high conductance, (ii) strong inhibition, and (iii) large fluctuations that arise from intermittent spiking events that are strongly correlated in time as well as in orientation domains, with the correlation time of the fluctuations controlled by the NMDA decay time scale. Our simulation results demonstrate that the IDS state captures numerically many aspects of experimental observation related to spontaneous ongoing activity, and the specific network mechanism of the IDS may suggest cortical mechanisms and the cortical operating point underlying observed spontaneous activity.",
keywords = "Fluctuation, Horizontal connection, Neuronal networks, Optical imaging, Spatiotemporal patterns",
author = "David Cai and Rangan, {Aaditya V.} and McLaughlin, {David W.}",
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AU - Cai, David

AU - Rangan, Aaditya V.

AU - McLaughlin, David W.

PY - 2005/4/19

Y1 - 2005/4/19

N2 - To investigate the existence and the characteristics of possible cortical operating points of the primary visual cortex, as manifested by the coherent spontaneous ongoing activity revealed by real-time optical imaging based on voltage-sensitive dyes, we studied numerically a very large-scale (≈5 × 105) conductance-based, integrate-and-fire neuronal network model of an ≈16-mm2 patch of 64 orientation hypercolumns, which incorporates both isotropic local couplings and lateral orientation-specific long-range connections with a slow NMDA component. A dynamic scenario of an intermittent desuppressed state (IDS) is identified in the computational model, which is a dynamic state of (i) high conductance, (ii) strong inhibition, and (iii) large fluctuations that arise from intermittent spiking events that are strongly correlated in time as well as in orientation domains, with the correlation time of the fluctuations controlled by the NMDA decay time scale. Our simulation results demonstrate that the IDS state captures numerically many aspects of experimental observation related to spontaneous ongoing activity, and the specific network mechanism of the IDS may suggest cortical mechanisms and the cortical operating point underlying observed spontaneous activity.

AB - To investigate the existence and the characteristics of possible cortical operating points of the primary visual cortex, as manifested by the coherent spontaneous ongoing activity revealed by real-time optical imaging based on voltage-sensitive dyes, we studied numerically a very large-scale (≈5 × 105) conductance-based, integrate-and-fire neuronal network model of an ≈16-mm2 patch of 64 orientation hypercolumns, which incorporates both isotropic local couplings and lateral orientation-specific long-range connections with a slow NMDA component. A dynamic scenario of an intermittent desuppressed state (IDS) is identified in the computational model, which is a dynamic state of (i) high conductance, (ii) strong inhibition, and (iii) large fluctuations that arise from intermittent spiking events that are strongly correlated in time as well as in orientation domains, with the correlation time of the fluctuations controlled by the NMDA decay time scale. Our simulation results demonstrate that the IDS state captures numerically many aspects of experimental observation related to spontaneous ongoing activity, and the specific network mechanism of the IDS may suggest cortical mechanisms and the cortical operating point underlying observed spontaneous activity.

KW - Fluctuation

KW - Horizontal connection

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KW - Optical imaging

KW - Spatiotemporal patterns

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