Next generation neural mass models
Standard neural mass models, such as Wilson- Cowan and Jansen-Rit, track the average activity of a population under the assumption that the neurons within the population are firing in synchrony. I am interested in building a realistic mean field model which does not assume synchrony and in turn tracks the degree of within population synchronisation. This can be achieved by considering θ-neuron model, which has recently been shown to admit to an exact mean-field description.
The objective of this work is to build a suitable model for understanding the generation of β-rhythms seen in motor cortex, and in turn β-rebound. β-rebound is a readily observed phenomenon that occurs in the motor cortex during movement. Upon movement initiation there is a drop in the beta band power, attributed to a loss of network synchrony. Roughly 0.5 seconds after movement termination the network resynchronises and the power rebounds. As synchrony is at the heart of β-rebound, developing a model that tracks population synchrony is of the utmost importance.