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.

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关于神经质量模型的有效性

对神经群的动力学建模是神经群体研究中的常用方法。各种模型已被证明可用于描述大量经验观察，包括自持局部振荡和远距离同步模式。我们讨论了质量模型在多大程度上真正类似于神经群体的平均动态。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物，我们会质疑神经质量模型的有效性。从嘈杂的泄漏集成和激发神经元网络开始，我们制定了两种不同的种群动态，它们都属于开创性的弗里曼神经质量模型类别。推导包含几个平均场假设和膜和突触动力学之间的时间尺度分离。我们将这些神经质量模型与群体的平均动态进行比较，揭示了兴奋性/抑制性神经元比例的界限以及质量模型的整体网络程度，以提供足够的估计。对于相当大的参数范围，我们的模型无法正确模拟神经网络的动力学，无论是处于非同步状态还是（高频）同步状态。只有在低频同步开始时，我们的模型才提供对平均潜在动态的正确估计。虽然这显示了它们的潜力，例如研究通过脑成像获得的静息状态动态，重点是过渡区域，