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Stimulus-dependent correlations and population codes.

Kresimir Josić1, Eric Shea-Brown, Brent Doiron

  • 1Department of Mathematics, University of Houston, Houston, TX 77204-3008, USA. josic@math.uh.edu

Neural Computation
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Summary
This summary is machine-generated.

Stimulus-dependent neural correlations impact how much information populations convey about stimuli. These correlations can directly carry information or modulate information from firing rates, significantly affecting neural coding.

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Area of Science:

  • Computational Neuroscience
  • Neural Coding
  • Information Theory

Background:

  • Neural population responses are influenced by stimulus-driven correlations between neurons.
  • The impact of stimulus-dependent correlations on neural information processing is not fully understood.

Purpose of the Study:

  • To investigate how stimulus-dependent correlations affect the information carried by neural populations.
  • To quantify the direct and modulatory roles of these correlations using Fisher information.

Main Methods:

  • Analysis of stimulus-dependent correlations in neural population responses.
  • Quantification of information using Fisher information framework.
  • Modeling the interplay between correlations, firing rates, and stimulus information.

Main Results:

  • Stimulus-dependent correlations can directly carry information, but often contribute minimally.
  • Modulatory effects of correlations on firing rate and variance information can be substantial.
  • Correlations increasing with firing rates can enhance population information, especially without spatial decay.

Conclusions:

  • Stimulus-dependent correlations play a crucial role in neural information processing.
  • The relationship between correlations and firing rates significantly influences information transmission.
  • Understanding these dynamics is key to deciphering neural population codes.