Neural mass models have been actively used since the 1970s to model the coarse-grained activity of large populations of neurons and synapses. They have proven especially fruitful for understanding brain rhythms.
However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue.
In this talk I will discuss a simple spiking neuron network model that has recently been shown to admit to an exact mean-field description for synaptic interactions.
This has many of the features of a neural mass model coupled to an additional dynamical equation that describes the evolution of population synchrony. I will show that this next generation neural mass model is ideally suited to understanding beta-rebound.
This is readily observed in MEG recordings whereby motor action causes a drop in the beta power band attributed to a loss of network synchrony. Existing neural mass models are unable to capture this phenomenon (event related de-synchrony) since they do not track any notion of network coherence (only firing rate).
I will spend the latter part of my talk discussing patterns and waves in a spatially continuous non-local extension of this model, highlighting its usefulness for large scale cortical modelling.
- Professor Stephen Coombes (University of Nottingham)
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