Next Generation Neural Mass Models

被引:90
作者
Coombes, Stephen [1 ]
Byrne, Aine [1 ]
机构
[1] Univ Nottingham, Sch Math Sci, Ctr Math Med & Biol, Univ Pk, Nottingham NG7 2RD, England
来源
NONLINEAR DYNAMICS IN COMPUTATIONAL NEUROSCIENCE | 2019年
关键词
SYNCHRONIZATION; NETWORKS; BRAIN;
D O I
10.1007/978-3-319-71048-8_1
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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 useful in 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 chapter we consider the.-neuron model that has recently been shown to admit to an exact mean-field description for instantaneous pulsatile interactions. We 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 a further dynamical equation that describes the evolution of network synchrony. A bifurcation analysis is used to uncover the primary mechanism for generating oscillations at the single and two population level. Numerical simulations also show that the phenomena of event related synchronisation and desynchronisation are easily realised. Importantly unlike its phenomenological counterpart this next generation neural mass model is an exact macroscopic description of an underlying microscopic spiking neurodynamics, and is a natural candidate for use in future large scale human brain simulations.
引用
收藏
页码:1 / 16
页数:16
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