神经科学应用的多尺度协同仿真设计模式

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Lionel Kusch, Sandra Diaz-Pier, Wouter Klijn, Kim Sontheimer, Christophe Bernard, Abigail Morrison, Viktor Jirsa
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引用次数: 0

摘要

整合不同来源的信息可以创造更多的科学价值。然而,跨时空尺度的数据、工具和模型的互操作性很难实现。在此,我们介绍了并行协同仿真工具箱,该工具箱可实现不同规模的仿真器之间的互操作。我们提供了一种软件科学协同设计模式,并以神经科学为例说明了它的功能,其中单个感兴趣的区域在细胞水平上进行模拟,使我们能够研究详细的机制,而其余网络则在群体水平上进行有效模拟。图中展示了虚拟大脑和 NEST 使用案例的工作流程,其中小鼠细胞级海马 CA1 区域被嵌入到涉及微观和宏观电极记录的完整大脑网络中。这一新工具允许在同一模拟框架中整合跨尺度知识,并根据多尺度实验对其进行验证,从而在很大程度上拓宽了计算模型的解释能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiscale co-simulation design pattern for neuroscience applications
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.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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