Medical School, University of Nicosia, Nicosia 2408, Cyprus; Bioinformatics Department, Cyprus Institute of Neurology and Genetics, Nicosia 1683, Cyprus.
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht 6229 ER, The Netherlands.
Neuroimage. 2021 Apr 1;229:117748. doi: 10.1016/j.neuroimage.2021.117748. Epub 2021 Jan 15.
Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.
伽马振荡被认为在神经元网络功能和神经元通信中发挥着关键作用，但迄今为止，其潜在的产生机制尚未完全阐明。至少部分原因可能是，即使在相互连接的抑制性（I）和兴奋性（E）神经元的简单网络模型中，许多参数仍然未知，并且是根据实际考虑或惯例设置的。在这里，我们通过要求 PING（锥体神经元网络伽马）模型同时满足基于跨越单个单元（尖峰）和局部群体（LFP）水平的经验数据的广泛的现实行为标准来解决这个问题，同时未知参数会发生变化。通过这样做，我们能够限制参数范围并选择经验有效的模型。得出的模型约束意味着，弱而非强 PING 作为伽马的产生机制，E 和 I 神经元之间的连接在特定范围内，以及 E 神经元的外部输入变化而不是 I 神经元。受约束的模型表现出有效的行为，包括随着对比度的增加而增加的伽马频率，以及在高对比度下的功率饱和或衰减。使用经验验证的模型，我们研究了在高对比度下伽马不稳定性的途径。这涉及到随着输入的增加，E 神经元的异质性增加，从而破坏 I 神经元起搏器功能。此外，我们说明了该模型解决有关伽马振荡特性和 GABA 电导代理的文献中的争议的能力。我们提出，我们在研究中得出的模型将对其他建模研究有用，并且当更丰富的经验数据集可用时，我们可以扩展对 PING 模型进行经验约束的方法。由于局部伽马网络是旨在通过其相互作用来理解复杂认知的更大网络的构建块，因此改进这些构建块的模型具有很大的价值。
