Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.
Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.
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DOI：
10.7554/elife.69013
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发表时间：
2022-07-06
期刊：
影响因子：
7.7
通讯作者：
Kotaleski, Jeanette Hellgren
中科院分区：
文献类型：
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作者：
Eriksson, Olivia;Bhalla, Upinder Singh;Blackwell, Kim T.;Crook, Sharon M.;Keller, Daniel;Kramer, Andrei;Linne, Marja-Leena;Saudargiene, Ausra;Wade, Rebecca C.;Kotaleski, Jeanette Hellgren
关键词：
神经科学中的建模发生在不同观点和方法的交叉点。通常，假设驱动的建模会聚焦一个问题，以便构建一个模型来研究关于系统如何工作或为什么观察到某些现象的特定假设。另一方面，数据驱动的建模遵循一种更无偏见的方法，模型构建由数据的计算密集型使用提供信息。与此同时，研究人员在不同的生物尺度和不同的抽象层次上使用模型。结合这些模型，同时根据实验数据验证它们，可以增加对多尺度大脑的理解。然而，缺乏互操作性，透明度和可重用性的模型和用于构建它们的工作流程创建的障碍，代表不同的生物尺度和使用不同的建模哲学构建的模型的集成。我们认为，同样的必要性，驱动资源和政策的数据-如公平（可查找，可扩展，互操作，可重用）的原则-也支持不同的建模方法的集成。FAIR原则要求以可查找、可解释、可互操作和可重用的格式共享数据。将这些原则应用于模型和建模工作流程，以及用于约束和验证它们的数据，将允许研究人员发现，重用，质疑，验证和扩展已发布的模型，无论它们是以现象学或机械方式实现的，作为几个方程还是作为多尺度，分层系统。为了说明这些想法，我们使用一个经典的突触可塑性模型，Bienenstock-Cooper-Munro规则，作为一个例子，由于其悠久的历史，不同层次的抽象，并在许多尺度上实现。
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.
DOI：
10.1016/j.str.2018.07.010
发表时间：
2018-10-02
期刊：
Structure (London, England : 1993)
影响因子：
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作者：
Wang B;Xie ZR;Chen J;Wu Y
通讯作者：
Wu Y
影响因子：
3
作者：
通讯作者：
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