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用于神经科学应用的多尺度协同仿真设计模式

Multiscale co-simulation design pattern for neuroscience applications.

作者信息

Kusch Lionel, Diaz-Pier Sandra, Klijn Wouter, Sontheimer Kim, Bernard Christophe, Morrison Abigail, Jirsa Viktor

机构信息

Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France.

Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany.

出版信息

Front Neuroinform. 2024 Feb 12;18:1156683. doi: 10.3389/fninf.2024.1156683. eCollection 2024.

Abstract

Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.

摘要

跨异构源整合信息可创造额外的科学价值。然而,数据、工具和模型的互操作性在空间和时间尺度上难以实现。在此,我们介绍工具箱并行协同仿真,它能使运行于不同尺度的模拟器实现互操作。我们提供一种软件科学协同设计模式,并通过一个神经科学示例说明其功能,其中在细胞水平模拟感兴趣的各个区域,使我们能够研究详细机制,而其余网络则在群体水平进行高效模拟。文中展示了针对虚拟大脑和NEST用例的工作流程,其中小鼠细胞水平海马体的CA1区域被嵌入到一个涉及微电极和宏电极记录的全脑网络中。这种新工具允许在同一仿真框架内跨尺度整合知识,并针对多尺度实验进行验证,从而极大地扩展了计算模型的解释力。

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