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Visualizing Visual Adaptation
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Adaptation modulates correlated subthreshold response variability in visual cortex.

Nathaniel C Wright1, Mahmood S Hoseini2, Ralf Wessel2

  • 1Department of Physics, Washington University in St. Louis, St. Louis, Missouri nathanielcalebwright@gmail.com.

Journal of Neurophysiology
|June 9, 2017
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Summary
This summary is machine-generated.

Neural circuits exhibit correlated variability in their responses. This study reveals that membrane potential correlations adapt to an intermediate level during visual processing, suggesting self-organization in cortical circuits.

Keywords:
adaptationcorrelated variabilitycortexmembrane potentialoscillations

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Cortical sensory responses show significant variability across presentations.
  • Correlated response variability (noise correlation) across neurons has implications for population coding.
  • Previous studies were limited by extracellular recordings, hindering detailed analysis of neural activity.

Purpose of the Study:

  • To investigate the dynamics and mechanisms of membrane potential-correlated variability (CC) in the visual cortex.
  • To explore how correlated variability adapts during visual processing at the membrane potential level.
  • To understand the role of intracortical mechanisms in shaping correlated variability.

Main Methods:

  • Dual whole-cell recordings from pairs of cortical pyramidal neurons in a turtle ex vivo brain preparation.
  • Measurement of membrane potential variability and its correlation during visual stimulation.
  • Development of a computational network model incorporating external inputs, synaptic depression, and network structure.

Main Results:

  • Visually evoked stimulation led to an increase in membrane potential-correlated variability, followed by a rapid return to baseline.
  • Correlated variability was observed to adapt towards an intermediate level during visual stimulation.
  • A computational model successfully reproduced the observed dynamics of correlated variability.

Conclusions:

  • Intracortical mechanisms, likely involving adaptation, mediate the dynamics of correlated variability.
  • Cortical circuits may self-organize towards a balanced regime that maintains intermediate correlated variability.
  • These findings provide insights into neural coding and network dynamics beyond spike-based analyses.