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Related Experiment Videos

Population coding in neuronal systems with correlated noise.

H Sompolinsky1, H Yoon, K Kang

  • 1Racah Institute of Physics and Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 12, 2001
PubMed
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Correlated noise in neuronal populations limits information coding. While positive correlations reduce accuracy, negative correlations enhance it, revealing a finite effective number of independent neurons even in large networks.

Area of Science:

  • Computational Neuroscience
  • Neural Coding
  • Information Theory

Background:

  • Neuronal representations of external events are distributed across cell populations.
  • Correlated noise is a significant factor influencing the accuracy of these population codes.
  • Understanding how population size and noise correlations affect coding accuracy is crucial.

Purpose of the Study:

  • To investigate the impact of correlated noise on the accuracy of neuronal population codes.
  • To determine if increasing population size (N) can suppress inherent coding errors in the presence of correlated noise.
  • To analyze the relationship between noise correlations and information capacity.

Main Methods:

  • Modeling a population of neurons broadly tuned to a 2D angular variable.

Related Experiment Videos

  • Simulating neuronal activity fluctuations as Gaussian noises with exponentially decaying pairwise correlations.
  • Utilizing Fisher information (FI) to quantify estimation error and information capacity.
  • Analyzing the system in the limit of large population size (N).

    • Main Results:

    • Positive correlations decrease the estimation capability compared to uncorrelated populations.
    • Information capacity saturates at a finite value as population size increases with positive correlations.
    • Negative correlations substantially increase the information capacity of neuronal populations.
    • The effective number of statistically independent degrees of freedom (N(eff)) remains finite for large N.

    Conclusions:

    • Correlated noise fundamentally limits the information capacity of neuronal populations.
    • Negative correlations can enhance coding efficiency, while positive correlations hinder it.
    • The effective number of independent neuronal units is constrained, even in large neural networks, with predictions for cortical areas suggesting N(eff) < ~100.