脑皮层锥体细胞间电紧张耦合的计算研究。

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2020-11-01 Epub Date: 2020-09-05 DOI:10.1007/s10827-020-00762-5
Jennifer Crodelle, Douglas Zhou, Gregor Kovačič, David Cai
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引用次数: 2

摘要

在成人皮层锥体细胞(PCs)之间是否存在电通讯,神经科学家们已经争论了几十年。皮层中间神经元间的间隙连接(Gap junction, GJs)在实验上得到了很好的证明,计算神经科学家和实验学家都提出了它们的功能作用。然而,皮层锥体细胞之间的类似连接的实验证据仍然难以捉摸,因为神经元之间的这些偶联明显罕见。在这项工作中,我们开发了一个神经元网络模型,该模型包括观察到的pc之间的电紧张耦合和中间神经元之间的间隙连接耦合的概率和强度,以及两个群体之间实际的突触连接。我们使用这个网络模型来研究脑皮层电张力耦合对网络行为的影响,目的是从理论上解决脑皮层电张力耦合脑皮层电张力耦合存在和作用的争议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A computational investigation of electrotonic coupling between pyramidal cells in the cortex.

The existence of electrical communication among pyramidal cells (PCs) in the adult cortex has been debated by neuroscientists for several decades. Gap junctions (GJs) among cortical interneurons have been well documented experimentally and their functional roles have been proposed by both computational neuroscientists and experimentalists alike. Experimental evidence for similar junctions among pyramidal cells in the cortex, however, has remained elusive due to the apparent rarity of these couplings among neurons. In this work, we develop a neuronal network model that includes observed probabilities and strengths of electrotonic coupling between PCs and gap-junction coupling among interneurons, in addition to realistic synaptic connectivity among both populations. We use this network model to investigate the effect of electrotonic coupling between PCs on network behavior with the goal of theoretically addressing this controversy of existence and purpose of electrotonically coupled PCs in the cortex.

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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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