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具有相似行为但电导密度不同的神经模型中的电流可视化。

Visualization of currents in neural models with similar behavior and different conductance densities.

机构信息

Volen Center and Biology Department, Brandeis University, Waltham, United States.

出版信息

Elife. 2019 Jan 31;8:e42722. doi: 10.7554/eLife.42722.

Abstract

Conductance-based models of neural activity produce large amounts of data that can be hard to visualize and interpret. We introduce visualization methods to display the dynamics of the ionic currents and to display the models' response to perturbations. To visualize the currents' dynamics, we compute the percent contribution of each current and display them over time using stacked-area plots. The waveform of the membrane potential and the contribution of each current change as the models are perturbed. To represent these changes over a range of the perturbation control parameter, we compute and display the distributions of these waveforms. We illustrate these procedures in six examples of bursting model neurons with similar activity but that differ as much as threefold in their conductance densities. These visualization methods provide heuristic insight into why individual neurons or networks with similar behavior can respond widely differently to perturbations.

摘要

基于电导率的神经活动模型会产生大量的数据,这些数据可能难以可视化和解释。我们引入了可视化方法来显示离子电流的动态,并显示模型对扰动的响应。为了可视化电流的动态,我们计算每个电流的百分比贡献,并使用堆叠面积图随时间显示它们。随着模型受到干扰,膜电位的波形和每个电流的贡献都会发生变化。为了在扰动控制参数的范围内表示这些变化,我们计算并显示这些波形的分布。我们在六个具有相似活动但电导密度相差三倍的爆发模型神经元的示例中说明了这些过程。这些可视化方法为为什么具有相似行为的单个神经元或网络对扰动的反应可能有很大差异提供了直观的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad9d/6395073/5d52c599e555/elife-42722-fig1.jpg

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