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Neurulation is the embryological process which forms the precursors of the central nervous system and occurs after gastrulation has established the three primary cell layers of the embryo: ectoderm, mesoderm, and endoderm. In humans, the majority of this system is formed via primary neurulation, in which the central portion of the ectoderm—originally appearing as a flat sheet of cells—folds upwards and inwards, sealing off to form a hollow neural tube. As development proceeds, the...
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Next-generation neural mass and field modeling.

Áine Byrne1,2, Reuben D O'Dea3, Michael Forrester3

  • 1Center for Neural Science, New York University, New York, New York.

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Summary
This summary is machine-generated.

This study introduces an advanced Wilson-Cowan neural population model that dynamically describes neural synchrony and firing rates. The model offers new insights into brain rhythms and functional connectivity, applicable to various neuroimaging studies.

Keywords:
beta reboundcortical wavesfunctional connectivityneural mass and field modelstranscranial magnetic stimulation

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Area of Science:

  • Computational Neuroscience
  • Neural Dynamics
  • Brain Rhythms

Background:

  • The Wilson-Cowan model is foundational for understanding neural population activity and brain rhythms.
  • Existing models often use sigmoidal firing rate functions, limiting dynamic descriptions of synchrony.
  • Large-scale brain network models increasingly integrate data from projects like the Human Connectome Project.

Purpose of the Study:

  • To develop a next-generation Wilson-Cowan style model that incorporates event-related synchronization and desynchronization.
  • To provide a dynamic description of neural synchrony evolution using the Kuramoto order parameter.
  • To link population firing rates to complex-valued population synchrony measures.

Main Methods:

  • Derived a mean-field model for a large population of quadratic integrate-and-fire neurons.
  • Replaced the traditional sigmoidal firing rate function with a real-valued function of a complex-valued synchrony measure.
  • Applied the model to analyze changes in power spectra during movement, functional connectivity at rest, and cortical wave propagation.

Main Results:

  • The new model dynamically describes the evolution of synchrony (Kuramoto order parameter) and population firing rates.
  • It successfully accounts for event-related synchronization and desynchronization phenomena.
  • The model provides insights into neuroimaging data from electro- and magnetoencephalography studies.

Conclusions:

  • This enhanced Wilson-Cowan model offers a more comprehensive framework for understanding neural population dynamics.
  • It provides valuable insights into brain rhythms, functional connectivity, and cortical dynamics.
  • The model is a powerful tool for computational neuroscience and analyzing complex neuroimaging data.