School of Mathematics and Statistics, Science Centre, University College Dublin, South Belfield, Dublin 4, Ireland.
School of Mathematical Sciences, Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, NG7 2RD, UK.
Brain Topogr. 2022 Jan;35(1):36-53. doi: 10.1007/s10548-021-00842-4. Epub 2021 May 15.
Neural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. 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. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.
神经群集模型自 20 世纪 70 年代以来就被用于对大量神经元的粗粒度活动进行建模。它们尤其有助于理解脑节律。然而，尽管这些模型的动机是基于神经生物学的考虑，但它们本质上是现象学的，无法重现真实神经元组织中所见的丰富反应。在这里，我们考虑了一个简单的尖峰神经元网络模型，该模型最近被证明可以对突触和间隙连接相互作用进行精确的平均场描述。平均场模型采用与标准神经群集模型类似的形式，另外还有一个动力学方程来描述群体内同步的演化。除了回顾这个下一代群集模型的起源，我们还讨论了将其扩展为描述理想化的空间扩展平面皮层的方法。为了强调该模型在 EEG/MEG 建模中的有用性，我们展示了如何使用它来揭示局部间隙连接耦合在塑造大规模突触波中的作用。
