海马Schaffer侧- ca1长期可塑性多巴胺能调节的计算模型。

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2022-02-01 Epub Date: 2021-08-25 DOI:10.1007/s10827-021-00793-6
Joseph T Schmalz, Gautam Kumar
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引用次数: 0

摘要

多巴胺在海马Schaffer侧侧- ca1锥体神经元突触(SC-CA1)的长期突触可塑性调节中起关键作用,SC-CA1是一种被广泛接受的学习和记忆的细胞模型。在过去40年的海马薄片实验中,有限的结果表明,SC-CA1突触中多巴胺D1/D5受体相对于高/低频刺激(HFS/LFS)的激活时间调节了这些突触中HFS/LFS诱导的长期增强/抑郁(LTP/LTD)的调节。然而,现有文献缺乏对不同浓度D1/D5激动剂以及D1/D5受体激活与HFS/LFS诱导LTP/LTD之间的相对时间点如何影响SC-CA1突触动力学的时空调节的完整描述。在本文中,我们首次建立了一个计算模型,定量预测了各种D1/D5激动剂对HFS/LFS诱导的SC-CA1突触LTP/LTD的时间剂量依赖性调节。我们的模型结合了电生理水平上的生化效应和电效应。在贝叶斯框架下,我们从发表的电生理数据中估计了模型参数,这些数据来自不同的海马CA1切片实验。我们的建模结果证明了我们的模型在不同HFS/LFS协议下对现有实验结果进行定量预测的能力。我们的模型预测表明,D1/D5激动剂调节的LTP/LTD与激活的D1/D5受体与HFS/LFS协议之间的相对时间以及D1/D5激动剂的应用浓度有很强的非线性依赖性。
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 model of dopaminergic modulation of hippocampal Schaffer collateral-CA1 long-term plasticity.

Dopamine plays a critical role in modulating the long-term synaptic plasticity of the hippocampal Schaffer collateral-CA1 pyramidal neuron synapses (SC-CA1), a widely accepted cellular model of learning and memory. Limited results from hippocampal slice experiments over the last four decades have shown that the timing of the activation of dopamine D1/D5 receptors relative to a high/low-frequency stimulation (HFS/LFS) in SC-CA1 synapses regulates the modulation of HFS/LFS-induced long-term potentiation/depression (LTP/LTD) in these synapses. However, the existing literature lacks a complete picture of how various concentrations of D1/D5 agonists and the relative timing between the activation of D1/D5 receptors and LTP/LTD induction by HFS/LFS, affect the spatiotemporal modulation of SC-CA1 synaptic dynamics. In this paper, we have developed a computational model, a first of its kind, to make quantitative predictions of the temporal dose-dependent modulation of the HFS/LFS induced LTP/LTD in SC-CA1 synapses by various D1/D5 agonists. Our model combines the biochemical effects with the electrical effects at the electrophysiological level. We have estimated the model parameters from the published electrophysiological data, available from diverse hippocampal CA1 slice experiments, in a Bayesian framework. Our modeling results demonstrate the capability of our model in making quantitative predictions of the available experimental results under diverse HFS/LFS protocols. The predictions from our model show a strong nonlinear dependency of the modulated LTP/LTD by D1/D5 agonists on the relative timing between the activated D1/D5 receptors and the HFS/LFS protocol and the applied concentration of D1/D5 agonists.

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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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