On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks.
On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks.
复制标题
关于局部皮质网络兴奋和抑制总体平衡的生理和结构贡献者。
DOI：
10.1101/2023.01.10.523489
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Choi,Hannah
中科院分区：
文献类型：
--
作者：
Shirani,Farshad;Choi,Hannah
皮质网络中兴奋和抑制的总体平衡是其功能和正常运作的核心。这种协调的兴奋和抑制的共同进化是通过神经元之间的复杂局部相互作用建立的，这些相互作用由特定的网络连接结构组织，并通过调节突触活动来动态控制。因此，确定这些结构和生理因素如何有助于建立兴奋和抑制的总体平衡，对于理解调节平衡的稳态可塑性机制至关重要。我们使用生物学上合理的数学模型来广泛研究多个关键因素对网络整体平衡的影响。我们的特征网络的基线平衡状态的某些功能特性，并演示了如何在网络的生理和结构参数的变化偏离这种平衡，特别是，导致在自发活动的网络过渡到高振幅慢振荡制度。我们表明，从参考平衡状态的偏差可以连续量化的平均兴奋性，平均抑制性突触电导的网络中测量的比率。我们的结果表明，通常观察到的局部皮质网络中抑制性神经元数量与兴奋性神经元数量的比例对于其稳定性和兴奋性来说几乎是最佳的。此外，抑制性突触衰减时间常数和神经元-抑制性网络连接密度的值对皮层网络的整体平衡和稳定性至关重要。然而，在我们的研究结果中，网络稳定性对突触量子电导的调制是足够强大的，因为它们在学习和记忆中的作用。我们的研究基于广泛的分叉分析，从而揭示了功能的最优性和关键性的结构和生理参数，在建立本地皮层网络的基线操作状态。
Overall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a network’s baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory. Our study based on extensive bifurcation analyses thus reveal the functional optimality and criticality of structural and physiological parameters in establishing the baseline operating state of local cortical networks.