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Optimal Encoding in Stochastic Latent-Variable Models.

Michael E Rule1, Martino Sorbaro2, Matthias H Hennig3

  • 1Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.

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Summary
This summary is machine-generated.

Statistical models of sensory coding in noisy spiking networks reveal adaptive strategies. Networks balance precision and noise-robustness, encoding informative stimuli with high precision while suppressing variability for frequent stimuli.

Keywords:
encodinginformation theoryneural networkssensory systems

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Area of Science:

  • Computational neuroscience
  • Information theory
  • Statistical physics

Background:

  • Neural systems function as sensory communication channels.
  • Neural population coding faces unique constraints beyond traditional communication theory.
  • Understanding these constraints is key to deciphering neural information processing.

Purpose of the Study:

  • To explore encoding strategies in noisy spiking neural networks using statistical models.
  • To investigate how neural networks adapt to stimuli with varying information content.
  • To examine the emergence of statistical criticality in neural population codes.

Main Methods:

  • Utilized restricted Boltzmann machines (RBMs) as a model for sensory encoding.
  • Analyzed the balance between precision and noise-robustness in neural network capacity.
  • Investigated the relationship between model size, input statistics, and emergent phenomena.

Main Results:

  • Networks with sufficient capacity learn to balance precision and noise-robustness.
  • Informative stimuli are encoded with higher precision, suppressing variability.
  • Frequent, less informative stimuli elicit more variable responses.
  • Statistical criticality emerges in neural population codes when input statistics are well captured.

Conclusions:

  • Learned encoding strategies adaptively balance precision and noise-robustness based on stimulus information content.
  • Variability suppression in neural coding mirrors observed phenomena in biological sensory systems.
  • Emergent statistical criticality in neural populations has thermodynamic interpretations and links to coding theories.