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Publication Detail
Learning shapes cortical dynamics to enhance integration of relevant sensory input
  • Publication Type:
    Journal article
  • Authors:
    Chadwick A, Khan AG, Poort J, Blot A, Hofer SB, Mrsic-Flogel TD, Sahani M
  • Publisher:
    Elsevier BV
  • Publication date:
  • Journal:
  • Status:
  • Print ISSN:
  • Language:
  • Keywords:
    neural coding, network dynamics, cortical circuits, learning, decision making, visual cortex, noise correlations, computational model, dynamical systems, sensory processing
  • Notes:
    Copyright © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.
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