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Researchers quantitatively characterized the order-disorder transition in leaky integrate-and-fire neural networks. They identified critical exponents and found neuronal avalanche scaling consistent with experimental data.

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Area of Science:

  • Computational Neuroscience
  • Statistical Physics

Background:

  • Leaky integrate-and-fire neural models exhibit firing pattern transitions with varying neuron coupling.
  • Quantitative characterization of these order-disorder transitions, including critical exponents, remains incomplete.

Purpose of the Study:

  • To quantitatively characterize the order-disorder transition in a network of excitatory neurons.
  • To determine the critical exponents of the continuous transition.
  • To explore criticality using neuronal avalanche analysis.

Main Methods:

  • Simulated a network of N excitatory neurons on a square lattice with local connections and periodic boundary conditions.
  • Introduced population-averaged voltage (PAV) and a Kuramoto order parameter (m) to track network activity.
  • Employed finite-size scaling analysis to calculate critical exponents.
  • Analyzed neuronal avalanche profiles at the critical point.

Main Results:

  • Identified a continuous transition from irregular to phase-locked spiking by increasing neuronal cooperation (K).
  • Determined the critical cooperation strength (K_c) and calculated critical exponents using finite-size scaling.
  • Neuronal avalanche profiles at K_c exhibited scaling behavior consistent with experimental findings.

Conclusions:

  • The study provides a quantitative characterization of the firing transition in a neural network model.
  • The findings offer insights into criticality in neural systems and align with experimental observations.
  • Neuronal avalanches serve as a valid indicator of criticality in this model.