Biologically realistic mean field model of spiking neural networks with fast and slow inhibitory synapses.

IF 1.5 4区医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Journal of Computational Neuroscience Pub Date : 2025-04-23 DOI:10.1007/s10827-025-00904-7

Claudio Di Geronimo, Alain Destexhe, Matteo Di Volo

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引用次数: 0

Abstract

We present a mean field model for a spiking neural network of excitatory and inhibitory neurons with fast GABA $_{A}$ and nonlinear slow GABA $_{B}$ inhibitory conductance-based synapses. This mean field model can predict the spontaneous and evoked response of the network to external stimulation in asynchronous irregular regimes. The model displays theta oscillations for sufficiently strong GABA $_{B}$ conductance. Optogenetic activation of interneurons and an increase of GABA $_{B}$ conductance caused opposite effects on the emergence of gamma oscillations in the model. In agreement with direct numerical simulations of neural networks and experimental data, the mean field model predicts that an increase of GABA $_{B}$ conductance reduces gamma oscillations. Furthermore, the slow dynamics of GABA $_{B}$ synapses regulates the appearance and duration of transient gamma oscillations, namely gamma bursts, in the mean field model. Finally, we show that nonlinear GABA $_{B}$ synapses play a major role to stabilize the network from the emergence of epileptic seizures.

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具有快速和慢速抑制性突触的脉冲神经网络的生物现实平均场模型。

我们提出了一个具有快速GABA a和非线性慢GABA B抑制性传导突触的兴奋性和抑制性神经元的尖峰神经网络的平均场模型。该平均场模型可以预测非同步不规则状态下神经网络对外界刺激的自发和诱发反应。该模型显示了足够强的伽马氨基丁酸B电导的θ振荡。中间神经元的光遗传激活和GABA - B电导的增加对模型中伽马振荡的出现产生相反的影响。与神经网络的直接数值模拟和实验数据一致，平均场模型预测gabab电导的增加会减少伽马振荡。此外，在平均场模型中，GABA - B突触的缓慢动力学调节了瞬态伽马振荡（即伽马暴）的出现和持续时间。最后，我们发现非线性GABA - B突触在癫痫发作时稳定神经网络方面起着重要作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Neuroscience 生物-神经科学

CiteScore

2.00

自引率

8.30%

发文量

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.