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Adaptation reduces variability of the neuronal population code

Farzad Farkhooi1,*, Eilif Muller2, and Martin P. Nawrot1

  • 1Neuroinformatics and Theoretical Neuroscience, Freie Universität Berlin and BCCN-Berlin, Germany
  • 2Brain Mind Institute, EPFL, Lausanne, Switzerland

  • *Corresponding author: farzad@zedat.fu-berlin.de
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Phys. Rev. E 83, 050905(R) – Published 19 May, 2011

DOI: https://doi.org/10.1103/PhysRevE.83.050905

Abstract

Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for generalized non-renewal processes to calculate the interval and count statistics of superimposed processes governed by a slow adaptation variable. For an ensemble of neurons with spike-frequency adaptation, this results in the regularization of the population activity and an enhanced postsynaptic signal decoding. We confirm our theoretical results in a population of cortical neurons recorded in vivo.

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      Information

      Phys. Rev. E 83, 050905(R)– Published 19 May, 2011
      • Received 16 July 2010

      ©2011 American Physical Society

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