扩展自适应指数积分-火模型对离子调节损伤的建模。

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2025-03-01 Epub Date: 2025-01-23 DOI:10.1007/s10827-025-00893-7
Damien Depannemaecker, Federico Tesler, Mathieu Desroches, Viktor Jirsa, Alain Destexhe
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引用次数: 0

摘要

为了模拟神经元膜兴奋性的动力学,可以考虑许多模型,从最详细的生物物理到最高水平的现象学描述。最近在单个神经元水平上的研究表明,考虑离子浓度等缓慢变量的演化是很重要的。将这样的模型简化为集成和激活家族的模型,然后再到大型网络模型是很有趣的。在本文中,我们引入了一种方法,通过在自适应指数积分-燃烧模型(AdEx)中添加第三个缓慢变量来考虑离子调节的损害。然后,我们实现并模拟了一个包含该模型的网络。我们发现这个网络能够产生正常和癫痫性放电。该模型可用于正常和病理状态的网络模拟设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models.

To model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states.

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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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