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Correlation and synchrony transfer in integrate-and-fire neurons: basic properties and consequences for coding.

Eric Shea-Brown1, Kresimir Josić, Jaime de la Rocha

  • 1Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.

Physical Review Letters
|March 21, 2008
PubMed
Summary
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Neural pairs transform correlated inputs into correlated spikes. This transfer is faster with higher spike variability and rate, but this changes at longer time scales.

Area of Science:

  • Computational neuroscience
  • Neural coding

Background:

  • Understanding how neural networks process correlated inputs is crucial for deciphering brain function.
  • Previous models often simplify neuron dynamics and interactions.

Purpose of the Study:

  • To investigate the mechanisms by which pairs of neurons transfer correlated input currents into correlated output spikes.
  • To analyze how factors like spike time variability, firing rate, and neuronal properties influence this correlation transfer.

Main Methods:

  • Simulations of neuronal pairs with both linear and nonlinear membrane models.
  • Analysis of spike trains to quantify correlation transfer under varying input statistics and neuronal parameters.
  • Inclusion of refractory period effects to explore their impact.

Related Experiment Videos

Main Results:

  • Correlation transfer is enhanced by spike time variability and firing rate at rapid timescales.
  • This dependence on variability diminishes at larger timescales.
  • Nonlinear membrane models and heterogeneous cell pairs exhibit similar trends.
  • Refractory effects introduce significant nonmonotonicities in correlation transfer.

Conclusions:

  • Neuronal correlation transfer is a dynamic process sensitive to timescale and intrinsic neuronal properties.
  • Refractory periods play a critical role in shaping the output correlation.
  • Findings have implications for understanding population coding and the neural encoding of time-varying stimuli.