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基于简化模型的丘脑网状核神经元动力学分析

Analysis of the Neuron Dynamics in Thalamic Reticular Nucleus by a Reduced Model.

作者信息

Wang Chaoming, Li Shangyang, Wu Si

机构信息

School of Psychology and Cognitive Sciences, Peking-Tsinghua Center for Life Sciences, IDG/McGovern Institute for Brain Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.

Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.

出版信息

Front Comput Neurosci. 2021 Nov 16;15:764153. doi: 10.3389/fncom.2021.764153. eCollection 2021.

Abstract

Strategically located between the thalamus and the cortex, the inhibitory thalamic reticular nucleus (TRN) is a hub to regulate selective attention during wakefulness and control the thalamic and cortical oscillations during sleep. A salient feature of TRN neurons contributing to these functions is their characteristic firing patterns, ranging in a continuum from tonic spiking to bursting spiking. However, the dynamical mechanism under these firing behaviors is not well understood. In this study, by applying a reduction method to a full conductance-based neuron model, we construct a reduced three-variable model to investigate the dynamics of TRN neurons. We show that the reduced model can effectively reproduce the spiking patterns of TRN neurons as observed and experiments, and meanwhile allow us to perform bifurcation analysis of the spiking dynamics. Specifically, we demonstrate that the rebound bursting of a TRN neuron is a type of "fold/homo-clinic" bifurcation, and the tonic spiking is the fold cycle bifurcation. Further one-parameter bifurcation analysis reveals that the transition between these discharge patterns can be controlled by the external current. We expect that this reduced neuron model will help us to further study the complicated dynamics and functions of the TRN network.

摘要

抑制性丘脑网状核(TRN)位于丘脑和皮层之间的关键位置,是清醒时调节选择性注意以及睡眠期间控制丘脑和皮层振荡的枢纽。TRN神经元对这些功能有贡献的一个显著特征是其特征性放电模式,范围从紧张性放电到爆发性放电连续变化。然而,这些放电行为背后的动力学机制尚未得到很好的理解。在本研究中,通过对基于全电导的神经元模型应用约化方法,我们构建了一个约化的三变量模型来研究TRN神经元的动力学。我们表明,该约化模型能够有效地重现观察到的和实验中的TRN神经元放电模式,同时使我们能够对放电动力学进行分岔分析。具体而言,我们证明TRN神经元的反弹爆发是一种“折叠/同宿”分岔,而紧张性放电是折叠周期分岔。进一步的单参数分岔分析表明,这些放电模式之间的转变可以由外部电流控制。我们期望这个约化的神经元模型将有助于我们进一步研究TRN网络复杂的动力学和功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1619/8635031/a7142e909236/fncom-15-764153-g0001.jpg

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