单神经元低维模型的研究进展。

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS
Ulises Chialva, Vicente González Boscá, Horacio G Rotstein
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引用次数: 2

摘要

经典的霍奇金-赫胥黎(HH)点神经元动作电位生成模型是四维的。它由描述膜电位动力学的四个常微分方程和与瞬态钠离子电流和延迟整流钾离子电流相关的三个门控变量组成。基于电导的HH型模型是经典HH模型的高维扩展。它们包括许多与其他离子电流类型相关的补充状态变量,并且能够描述额外的现象,如亚阈值振荡,混合模式振荡(亚阈值振荡与尖峰穿插),群集和爆裂。在本文中,我们讨论了生物物理上可信的和现象学上的简化模型,这些模型保留了HH型模型的生物物理和/或动态描述以及产生复杂现象的能力,但有效维度(状态变量)的数量较低。我们描述了几个有代表性的模型。我们还描述了从HH型模型导出简化模型的系统和启发式方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Low-dimensional models of single neurons: a review.

Low-dimensional models of single neurons: a review.

The classical Hodgkin-Huxley (HH) point-neuron model of action potential generation is four-dimensional. It consists of four ordinary differential equations describing the dynamics of the membrane potential and three gating variables associated to a transient sodium and a delayed-rectifier potassium ionic currents. Conductance-based models of HH type are higher-dimensional extensions of the classical HH model. They include a number of supplementary state variables associated with other ionic current types, and are able to describe additional phenomena such as subthreshold oscillations, mixed-mode oscillations (subthreshold oscillations interspersed with spikes), clustering and bursting. In this manuscript we discuss biophysically plausible and phenomenological reduced models that preserve the biophysical and/or dynamic description of models of HH type and the ability to produce complex phenomena, but the number of effective dimensions (state variables) is lower. We describe several representative models. We also describe systematic and heuristic methods of deriving reduced models from models of HH type.

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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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