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Relating normalization to neuronal populations across cortical areas.

Douglas A Ruff1, Joshua J Alberts2, Marlene R Cohen2

  • 1Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania ruffd@pitt.edu.

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|July 1, 2016
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Summary

Neuronal normalization, a key brain process, varies between neurons. Stronger normalization correlates with distinct network interactions and topographic organization within cortical areas.

Keywords:
multielectrode recordingsnoise correlationnormalizationvisual cortex

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Normalization is a fundamental neural computation scaling responses to stimuli.
  • Existing models focus on trial-averaged data and lack mechanistic insight.
  • Normalization exhibits variability across neurons even under identical conditions.

Purpose of the Study:

  • To investigate the neural mechanisms and network interactions underlying normalization.
  • To explore the relationship between normalization strength and neuronal variability.
  • To determine if normalization exhibits topographic organization within cortical areas.

Main Methods:

  • Recorded multi-unit activity from three cortical areas in rhesus monkeys.
  • Monkeys viewed superimposed drifting gratings to elicit normalization.
  • Analyzed trial-to-trial variability and spatial proximity of neurons with varying normalization.

Main Results:

  • Neurons with strong normalization showed less trial-to-trial variability with local neurons.
  • These neurons exhibited more variability with neurons in distant cortical areas.
  • Normalization strength was spatially organized, with nearby neurons showing similar normalization.

Conclusions:

  • Normalization reflects a neuron's functional role within its local neural network.
  • Normalization, like sensory tuning, is topographically organized across cortical areas.
  • These findings provide mechanistic insights into normalization and network interactions.