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Neural networks transmit information via collective neuron activity, but correlations complicate analysis. This study introduces a novel method to calculate information in large neural populations, revealing insights into noise and memory effects.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Information Theory

Background:

  • Neural networks encode stimuli through collective spiking activity.
  • Population responses exhibit noise and complex correlations, hindering information transmission analysis.
  • Existing methods are limited to small neuronal groups due to dimensionality challenges.

Purpose of the Study:

  • To develop a scalable method for computing stimulus information in large neural populations.
  • To analytically characterize the influence of neuronal correlations on information encoding.
  • To apply the method to real-world neural data for understanding noise and memory effects.

Main Methods:

  • Developed a small-correlation expansion approximation.
  • Derived analytical expressions for stimulus information based on firing rates and pairwise correlations.
  • Validated the approximation using synthetic data and electrophysiological recordings from the vertebrate retina.

Main Results:

  • The small-correlation expansion accurately computes stimulus information in large neural populations.
  • The method provides interpretable analytical expressions.
  • Quantified the impact of noise correlations and single-neuron memory on information transmission in retinal data.

Conclusions:

  • The developed approximation overcomes the curse of dimensionality for analyzing information in large neural populations.
  • This approach offers a powerful tool for dissecting the roles of correlations and single-neuron properties in neural coding.
  • The findings have implications for understanding neural computation in sensory systems like the retina.