Abstract
In electrophysiological recordings of the brain, the transition from high amplitude to low amplitude signals are most likely caused by a change in the synchrony of underlying neuronal population firing patterns. Classic examples of such modulations are the strong stimulus-related oscillatory phenomena known as the movement related beta decrease (MRBD) and post-movement beta rebound (PMBR). A sharp decrease in neural oscillatory power is observed during movement (MRBD) followed by an increase above baseline on movement cessation (PMBR). MRBD and PMBR represent important neuroscientific phenomena which have been shown to have clinical relevance. Here, we present a parsimonious model for the dynamics of synchrony within a synaptically coupled spiking network that is able to 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 in terms of four ordinary differential equations that allows considerable insight to be obtained into the cause of the experimentally observed time-lag from movement termination to the onset of PMBR (∼ 0.5 s), as well as the subsequent long duration of PMBR (∼ 1 − 10 s). Our model represents the first to predict these commonly observed and robust phenomena and represents a key step in their understanding, in health and disease.
Highlights
The modelling of brain rhythms is a well established and vibrant part of computational neuroscience
We have presented a mechanistic model that exhibits both movement related beta decrease (MRBD) and post-movement beta rebound (PMBR)
This low dimensional model is derived from a corresponding high dimensional spiking network model and maintains a faithful representation of synaptic currents
Summary
The modelling of brain rhythms is a well established and vibrant part of computational neuroscience. For questions that relate to understanding the coarse grained activity of either synaptic currents, mean membrane potentials or population firing rates, it is more natural to appeal to neural mass models, as reviewed in Coombes (2010) The latter have proven especially fruitful in providing large scale descriptions of how neural activity evolves over both space as well as time (Coombes et al 2014; Pinotsis et al 2014). Given the dense connections of connections in cortex on small scales (Klinshov et al 2014) the global coupling assumption is not so restrictive for our purposes, though the assumption of fast synapses should be relaxed to incorporate more realistic post synaptic responses This is precisely the issue we address here to develop a model capable of explaining MRBD and PMBR.
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