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Discrete Synaptic Events Induce Global Oscillations in Balanced Neural Networks.

Denis S Goldobin1,2, Matteo di Volo3, Alessandro Torcini4,5,6

  • 1<a href="https://ror.org/03ymmms77">Institute of Continuous Media Mechanics</a>, Ural Branch of RAS, Academician Korolev street 1, 614013 Perm, Russia.

Physical Review Letters
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Summary
This summary is machine-generated.

This study introduces a new mean-field model for neural networks that accounts for synaptic shot noise. The model accurately predicts global oscillations in neural dynamics, outperforming the standard diffusion approximation.

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Area of Science:

  • Computational Neuroscience
  • Theoretical Neuroscience
  • Network Dynamics

Background:

  • Neural dynamics are driven by discrete synaptic events.
  • Current models often use diffusion approximation, treating synaptic inputs as Gaussian noise.
  • This simplification may not capture all emergent network behaviors.

Purpose of the Study:

  • To develop a mean-field formalism that includes synaptic shot noise.
  • To analyze neural dynamics in sparse balanced networks.
  • To compare the predictions of the new model with the diffusion approximation.

Main Methods:

  • Derivation of a mean-field formalism incorporating synaptic shot noise.
  • Analysis of sparse balanced neural networks.
  • Investigation of network transitions under varying excitatory drive and inhibitory feedback.

Main Results:

  • The developed formalism accurately predicts global oscillations emerging via continuous or hysteretic transitions.
  • These oscillations are observed under low excitatory drive and high inhibitory feedback.
  • The diffusion approximation fails to predict these emergent oscillations.
  • At low in-degrees, oscillation nature shifts from drift-driven to cluster activation.

Conclusions:

  • Synaptic shot noise is crucial for accurately modeling emergent global oscillations in neural networks.
  • The derived mean-field approach provides a more accurate description than the diffusion approximation.
  • Network behavior, including oscillation characteristics, is sensitive to input statistics and network topology.