A model of on/off transitions in neurons of the deep cerebellar nuclei: deciphering the underlying ionic mechanisms.

IF 2.3 4区 医学 Q1 Neuroscience
Hugues Berry, Stéphane Genet
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引用次数: 2

Abstract

The neurons of the deep cerebellar nuclei (DCNn) represent the main functional link between the cerebellar cortex and the rest of the central nervous system. Therefore, understanding the electrophysiological properties of DCNn is of fundamental importance to understand the overall functioning of the cerebellum. Experimental data suggest that DCNn can reversibly switch between two states: the firing of spikes (F state) and a stable depolarized state (SD state). We introduce a new biophysical model of the DCNn membrane electro-responsiveness to investigate how the interplay between the documented conductances identified in DCNn give rise to these states. In the model, the F state emerges as an isola of limit cycles, i.e. a closed loop of periodic solutions disconnected from the branch of SD fixed points. This bifurcation structure endows the model with the ability to reproduce the [Formula: see text] transition triggered by hyperpolarizing current pulses. The model also reproduces the [Formula: see text] transition induced by blocking Ca currents and ascribes this transition to the blocking of the high-threshold Ca current. The model suggests that intracellular current injections can trigger fully reversible [Formula: see text] transitions. Investigation of low-dimension reduced models suggests that the voltage-dependent Na current is prominent for these dynamical features. Finally, simulations of the model suggest that physiological synaptic inputs may trigger [Formula: see text] transitions. These transitions could explain the puzzling observation of positively correlated activities of connected Purkinje cells and DCNn despite the former inhibit the latter.

Abstract Image

Abstract Image

Abstract Image

小脑深部核神经元的开/关转换模型:解读潜在的离子机制。
小脑深部核(DCNn)的神经元代表了小脑皮层和中枢神经系统其余部分之间的主要功能联系。因此,了解DCNn的电生理特性对于了解小脑的整体功能至关重要。实验数据表明,DCNn可以可逆地在两种状态之间切换:尖峰放电(F态)和稳定的去极化状态(SD态)。我们引入了一种新的DCNn膜电响应的生物物理模型,以研究DCNn中记录的电导之间的相互作用如何引起这些状态。在模型中,F态表现为一个极限环孤立体,即与SD不动点分支断开的周期解闭环。这种分岔结构使模型能够重现由超极化电流脉冲触发的[公式:见文本]过渡。该模型还再现了由阻断Ca电流引起的转变,并将这种转变归因于阻断高阈值Ca电流。该模型表明,细胞内电流注入可以触发完全可逆的[公式:见文本]转变。对低维降维模型的研究表明,电压依赖的钠电流是这些动态特征的突出体现。最后,该模型的模拟表明,生理突触输入可能触发[公式:见文本]转换。这些转变可以解释连接的浦肯野细胞和DCNn的正相关活动的令人困惑的观察,尽管前者抑制后者。
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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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