兴奋性和抑制性神经元网络伽马频率振荡的均场模型

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2024-05-01 Epub Date: 2024-03-21 DOI:10.1007/s10827-024-00867-1
Farzin Tahvili, Alain Destexhe
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引用次数: 0

摘要

伽马振荡广泛存在于唤醒-睡眠周期不同状态下的大脑皮层中,被认为在感觉处理和认知中发挥作用。在这里,我们从两个层面研究了伽马振荡的出现:尖峰神经元网络和均场模型。在网络层面,我们考虑了产生伽马振荡的两种不同机制,结果表明,如果考虑到神经元之间的突触延迟,伽马振荡就会出现。在平均场层面,我们证明通过引入延迟,平均场也能产生伽马振荡。在这两种机制中,均值场都能匹配尖峰网络中兴奋和抑制群的平均活动及其振荡频率。这种伽马振荡的均值场模型应该是研究大脑中通过伽马振荡进行大规模相互作用的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A mean-field model of gamma-frequency oscillations in networks of excitatory and inhibitory neurons.

Gamma oscillations are widely seen in the cerebral cortex in different states of the wake-sleep cycle and are thought to play a role in sensory processing and cognition. Here, we study the emergence of gamma oscillations at two levels, in networks of spiking neurons, and a mean-field model. At the network level, we consider two different mechanisms to generate gamma oscillations and show that they are best seen if one takes into account the synaptic delay between neurons. At the mean-field level, we show that, by introducing delays, the mean-field can also produce gamma oscillations. The mean-field matches the mean activity of excitatory and inhibitory populations of the spiking network, as well as their oscillation frequencies, for both mechanisms. This mean-field model of gamma oscillations should be a useful tool to investigate large-scale interactions through gamma oscillations in the brain.

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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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