Biomathematics / Computational Biology Colloquium
Next generation neural mass modelling
Speaker: Áine Byrne, Center for Neural Science, New York University
Location: Warren Weaver Hall 1314
Date: Tuesday, October 31, 2017, 12:30 p.m.
Electromagnetic recordings of the brain that show transitions from high amplitude to low amplitude signals are likely caused by an underlying changes in the synchrony of neuronal population firing patterns. A classic example is the event-related oscillatory phenomenon known as post-movement beta-rebound (PMBR), where a sharp increase in EEG or MEG power is seen at beta frequency following movement termination. A related phenomenon is movement related beta decrease (MRBD), whereby beta rhythms are suppressed during movement.
Traditionally neural mass models have been used to model large-scale brain dynamics, however they fail to account for the degree of synchronisation within a population. This could be tracked within a large-scale model of synaptically interacting conductance based neurons, though at the expense of analytical tractability. Thus it is of interest to seek levels of description that provide a bridge between microscopic single neuron dynamics and coarse grained neural mass models, while preserving some notion of within-population coherence.
I will present a parsimonious model for the dynamics of synchrony within a synaptically coupled spiking network that can replicate a human MEG power spectrogram showing the evolution from MRBD to PMBR. Importantly the high-dimensional spiking model has an exact mean field description that allows considerable insight into the cause of beta-rebound. Interestingly the reduced model takes the form of a generalised neural mass model where the standard sigmoidal firing rate has been replaced by a derived quantity that is a function of the Kuramoto order parameter for synchrony.