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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
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Slow diffusive dynamics in a chaotic balanced neural network.

Nimrod Shaham1, Yoram Burak1,2

  • 1Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel.

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Summary
This summary is machine-generated.

Neural networks in the balanced state can maintain continuous parameter working memory. Chaotic noise causes memory degradation, but large networks with tuned connections extend memory timescales significantly.

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Area of Science:

  • Computational Neuroscience
  • Neural Dynamics

Background:

  • Cortical neural noise is proposed to originate from chaotic dynamics in the balanced state, where excitatory and inhibitory inputs nearly cancel.
  • The ability of balanced state networks to perform noise-sensitive tasks like continuous parameter working memory, while accounting for irregular single-neuron firing, is not well understood.

Purpose of the Study:

  • To investigate if neural networks in the balanced state can support continuous parameter working memory.
  • To analyze the network architecture and dynamics enabling such memory storage and potential degradation.

Main Methods:

  • Analytical derivation in the limit of an infinite network to identify steady balanced states.
  • Analysis of finite networks to characterize chaotic noise-driven diffusive motion along attractors.
  • Calculation of diffusivity and its dependence on system size.

Main Results:

  • A simple neural circuit architecture allows continuous parameter working memory in the balanced state.
  • Infinite networks exhibit a continuous set of steady balanced states for indefinite parameter storage.
  • In finite networks, chaotic noise induces diffusive motion, degrading memory; diffusivity is inversely proportional to system size.
  • Slow diffusive motion leads to slowly decaying temporal cross-correlations distinct from previous balanced state descriptions.

Conclusions:

  • The proposed balanced network architecture can sustain continuous parameter working memory.
  • For sufficiently large neural populations and tuned connections, memory timescales can be extended by orders of magnitude beyond single-neuron timescales.
  • This model reconciles irregular neural firing with robust working memory in the balanced state.