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Five key factors determining pairwise correlations in visual cortex.

David P A Schulz1, Maneesh Sahani2, Matteo Carandini3

  • 1COMPLeX, London, United Kingdom; Gatsby Computational Neuroscience Unit, London, United Kingdom; and Institute of Ophthalmology, University College London, London, United Kingdom schulz.dpa@gmail.com.

Journal of Neurophysiology
|May 29, 2015
PubMed
Summary
This summary is machine-generated.

Neural noise correlations in the visual cortex are influenced by five key factors, including firing rate and spike width. Understanding these factors is crucial for decoding population activity in the brain.

Keywords:
functional connectivitynatural stimulisensory cortexspontaneous activityvariability

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neural responses exhibit variability and correlations, termed noise correlations, reflecting population dynamics.
  • These correlations are thought to be fundamental to sensory processing and information encoding.

Purpose of the Study:

  • To investigate the factors influencing noise correlations between pairs of neurons in the primary visual cortex (V1).
  • To quantify the relative contributions of various factors to pairwise neuronal correlations.

Main Methods:

  • Analysis of 22,705 neuronal pairs in anesthetized cat V1 during spontaneous and stimulus-evoked activity.
  • Application of a nonlinear additive model to disentangle the influences of 11 potential factors on noise correlations.
  • Examination of responses to artificial and natural visual stimuli.

Main Results:

  • Five factors predominantly determine pairwise noise correlations: cortical distance, sensory tuning difference, firing rate, spike width, and spike isolation.
  • Cortical distance and tuning difference decrease correlations, while firing rate and spike width increase them.
  • Poor spike isolation significantly increases correlations, especially for neurons recorded on different electrodes.

Conclusions:

  • Noise correlations in V1 are shaped by a combination of intrinsic neuronal properties and network interactions.
  • Spike isolation emerges as a critical factor, particularly for simultaneously recorded neurons.
  • These findings provide a unified framework for understanding population coding in sensory cortices.