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Structural phase transitions in neural networks.

Tatyana S Turova1

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Summary
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This study models neural networks as stochastic processes on random graphs, finding a balance between excitation and inhibition is key for forming synfire chains.

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Area of Science:

  • Computational Neuroscience
  • Complex Systems
  • Network Science

Background:

  • Neural networks are complex systems with emergent properties.
  • Understanding the relationship between network structure and dynamics is crucial.
  • Integrate-and-fire models are commonly used to simulate neuronal activity.

Purpose of the Study:

  • To model a neural network as a stochastic process on a random graph.
  • To investigate the dependence between initial connection sparseness and network activation dynamics.
  • To identify conditions supporting the formation of synfire chains.

Main Methods:

  • Developed a stochastic model of a neural network using random graphs.
  • Represented neurons using integrate-and-fire processes.
  • Analyzed the relationship between connection probabilities, network activity, and sparseness.

Main Results:

  • The graph structure, representing neural connections, is activity-dependent.
  • A critical balance between network excitation and inhibition was identified.
  • This balanced regime facilitates the formation of synfire chains.

Conclusions:

  • The interplay between network structure and dynamics is essential for information processing.
  • Balanced excitation-inhibition provides a mechanism for robust signal propagation (synfire chains).
  • This model offers insights into the self-organization principles of neural systems.