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Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability.

Meng Li1,2, Joe Z Tsien1,2

  • 1Brain and Behavior Discovery Institute, Medical College of Georgia, Augusta UniversityAugusta, GA, United States.

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Summary
This summary is machine-generated.

Neuronal variability, often dismissed as noise, actually encodes information via the Neural Self-Information Theory. This theory proposes that inter-spike interval (ISI) durations carry discrete information, enabling real-time neural coding and cell-assembly pattern decoding.

Keywords:
cell assemblycode of silenceneural codeneural computingneural spike variabilityself-informationsurprisal codevariability-surprisal

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Information Theory

Background:

  • Neuronal variability in spike timing is a significant challenge in understanding real-time neural coding.
  • Traditionally, this variability is treated as noise and averaged out in analyses.
  • Existing neural coding models (rate, population, temporal) often rely on external reference points.

Purpose of the Study:

  • To introduce the Neural Self-Information Theory, proposing that neuronal variability is intrinsically informative.
  • To elucidate how variability in inter-spike intervals (ISI) is self-tagged with discrete information.
  • To present a novel, intrinsic neural coding principle that does not require external observers.

Main Methods:

  • Conceptual framework development based on the self-information principle.
  • Analysis of inter-spike interval (ISI) variability and its probability distributions.
  • Exploration of the link between ISI variability, intracellular processes, and gene expression.
  • Theoretical modeling of cell-assembly codes arising from coordinated ISI surprisals.

Main Results:

  • Neural coding operates on a self-information principle where ISI variability carries discrete information.
  • Low-probability, rare-occurrence ISIs (surprisals) convey the most information.
  • High-probability ISIs reflect a balanced 'ground state' and convey minimal information.
  • This intrinsic code is coupled with cellular dynamics, including biochemical cascades and gene expression regulation.

Conclusions:

  • The proposed silence variability-based self-information code is intrinsic to neurons.
  • Temporally coordinated ISI surprisals across neuronal populations can form robust cell-assembly codes.
  • This framework offers a general decoding strategy for uncovering cell-assembly patterns underlying various variables in real-time.
  • The theory challenges the conventional view of neuronal variability as mere noise.