Deschle Nicolás, Ignacio Gossn Juan, Tewarie Prejaas, Schelter Björn, Daffertshofer Andreas
Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences & Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Institute for Complex Systems and Mathematical Biology, University of Aberdeen, King's College, Aberdeen, United Kingdom.
Front Comput Neurosci. 2021 Jan 5;14:581040. doi: 10.3389/fncom.2020.581040. eCollection 2020.
Modeling the dynamics of neural masses is a common approach in the study of neural populations. Various models have been proven useful to describe a plenitude of empirical observations including self-sustained local oscillations and patterns of distant synchronization. We discuss the extent to which mass models really resemble the mean dynamics of a neural population. In particular, we question the validity of neural mass models if the population under study comprises a mixture of excitatory and inhibitory neurons that are densely (inter-)connected. Starting from a network of noisy leaky integrate-and-fire neurons, we formulated two different population dynamics that both fall into the category of seminal Freeman neural mass models. The derivations contained several mean-field assumptions and time scale separation(s) between membrane and synapse dynamics. Our comparison of these neural mass models with the averaged dynamics of the population reveals bounds in the fraction of excitatory/inhibitory neuron as well as overall network degree for a mass model to provide adequate estimates. For substantial parameter ranges, our models fail to mimic the neural network's dynamics proper, be that in de-synchronized or in (high-frequency) synchronized states. Only around the onset of low-frequency synchronization our models provide proper estimates of the mean potential dynamics. While this shows their potential for, e.g., studying resting state dynamics obtained by encephalography with focus on the transition region, we must accept that predicting the more general dynamic outcome of a neural network via its mass dynamics requires great care.
对神经群体动力学进行建模是神经群体研究中的一种常见方法。各种模型已被证明有助于描述大量的实证观察结果，包括自持局部振荡和远距离同步模式。我们讨论了群体模型在多大程度上真正类似于神经群体的平均动力学。特别是，如果所研究的群体包含密集（相互）连接的兴奋性和抑制性神经元的混合体，我们质疑神经群体模型的有效性。从一个有噪声的泄漏积分发放神经元网络出发，我们制定了两种不同的群体动力学，它们都属于开创性的弗里曼神经群体模型范畴。推导过程包含了几个平均场假设以及膜动力学和突触动力学之间的时间尺度分离。我们将这些神经群体模型与群体的平均动力学进行比较，揭示了兴奋性/抑制性神经元比例以及群体模型提供充分估计所需的整体网络度的界限。在相当大的参数范围内，我们的模型无法正确模拟神经网络的动力学，无论是在去同步状态还是在（高频）同步状态。只有在低频同步开始时，我们的模型才能提供平均电位动力学的正确估计。虽然这表明了它们在例如研究通过脑电图获得的静息状态动力学方面的潜力，重点是过渡区域，但我们必须承认，通过其群体动力学预测神经网络更一般的动态结果需要格外谨慎。
