Next generation neural field modelling

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 first discuss a theta-neuron network model that has recently been shown to admit to an exact mean-field description for instantaneous pulsatile interactions. I will then show that the inclusion of a more realistic synapse model leads to a mean-field model that has many of the features of a neural mass model coupled to an additional dynamical equation that describes the evolution of network synchrony. I will further show that this next generation neural mass model is ideally suited to understanding beta-rebound. This is readily observed in MEG recordings whereby hand movement 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 since they do not track any notion of network coherence (only firing rate). I will finish my talk by presenting some results for the spatio-temporal pattern formation properties of a neural field version of this model.