随机共振在双室海马锥体神经元模型中的出现。

IF 2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2022-05-01 Epub Date: 2022-01-13 DOI:10.1007/s10827-021-00808-2
Muhammad Bilal Ghori, Yanmei Kang, Yaqian Chen
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引用次数: 10

摘要

体外研究表明,海马锥体神经元采用类似于随机共振(SR)的机制来增强远端突触产生的弱刺激的检测和传递。为了从多室模型分析的角度支持实验结果,本文旨在阐明噪声双室海马锥体神经元模型中的SR现象,该模型是Pinsky-Rinzel神经元模型的一个变体,具有光滑的激活函数和超极化激活的阳离子电流。通过对模型的分岔分析,我们揭示了导致SR发生的潜在动力结构。此外,使用随机产生的双相脉冲序列和Orenstein-Uhlenbeck过程产生的宽带噪声作为噪声扰动,我们观察并量化了SR和超阈值SR。频谱分析表明,除固有神经动力学外,噪声扰动下的频谱功率分布是影响SR行为的主要因素。本研究结果提示,噪声增强了与海马锥体神经元长形树突结构相关的弱刺激的传递,从而为相关的实验室发现提供了支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emergence of stochastic resonance in a two-compartment hippocampal pyramidal neuron model.

In vitro studies have shown that hippocampal pyramidal neurons employ a mechanism similar to stochastic resonance (SR) to enhance the detection and transmission of weak stimuli generated at distal synapses. To support the experimental findings from the perspective of multicompartment model analysis, this paper aimed to elucidate the phenomenon of SR in a noisy two-compartment hippocampal pyramidal neuron model, which was a variant of the Pinsky-Rinzel neuron model with smooth activation functions and a hyperpolarization-activated cation current. With a bifurcation analysis of the model, we demonstrated the underlying dynamical structure responsible for the occurrence of SR. Furthermore, using a stochastically generated biphasic pulse train and broadband noise generated by the Orenstein-Uhlenbeck process as noise perturbation, both SR and suprathreshold SR were observed and quantified. Spectral analysis revealed that the distribution of spectral power under noise perturbations, in addition to inherent neurodynamics, is the main factor affecting SR behavior. The research results suggested that noise enhances the transmission of weak stimuli associated with elongated dendritic structures of hippocampal pyramidal neurons, thereby providing support for related laboratory findings.

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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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