Next-generation neural mass and field modeling

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.