CRCNS Data Sharing: Exchange and Evaluation of Reduced Neuron Modles

CRCNS数据共享：简化神经元模型的交换和评估

• 批准号: 9052452
• 负责人: Richard C Gerkin
• 金额: $ 12.84万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9052452
• 关键词: Address; Agreement; Behavior; Biological; Biological Process; Biology; Brain; Code; Collaborations; Complement; Complex; Computer Analysis; Computer Simulation; Data; Data Set; Data Sources; Entropy; Environment; Equation; Evaluation; Functional disorder; Grouping; Hand; Imagery; Information Dissemination; Intuition; Language; Literature; Manuscripts; Measures; Membrane Potentials; Modeling; Neurons; Neurosciences; Online Systems; Performance; Physiological; Physiology; Procedures; Process; Publications; Publishing; Reproducibility; Research; Research Personnel; Running; Source; Specific qualifier value; Speed; Stimulus; Students; Targeted Research; Test Result; Testing; Textbooks; Time; Validation; Work; abstracting; adjudicate; computer program; data modeling; data sharing; information model; insight; interest; journal article; models and simulation; novel; operation; programs; research study; response; simulation; software development; text searching; tool; uptake; web interface

 DESCRIPTION (provided by applicant): Theoretical neuroscientists use neuron models to predict, understand, and explain biological neuron behavior. They often work with "reduced" neuron models that abstract away biological details but capture essential neuronal dynamics. This choice facilitates mathematical tractability, conceptual analysis, and computational speed. However, the tradeoffs inherent in using such models (instead of biologically detailed ones) are not transparent. It is often unclear if a model is faithful to essential observed dynamics of the neuron, and if so, under what model parameters and stimulus conditions. It is also rare for multiple types of reduced models to be compared in this regard, making it difficult to select the most appropriate one for a scientific question. Lastly, such models, once developed and parameterized, usually are not shared among researchers in a form that facilitates reproducibility and re-use, nor can they be easily discovered. As a result, status quo behavior in the use of reduced models is often simply to choose a "favorite" model regardless of merit, to optimize it for the scientific question at hand, and then to discard it. A standard practice in professional software development is unit testing. A unit test is a procedure that validates a single component of a computer program against a single correctness criterion. An ongoing effort to develop an analogous unit-testing procedure for neuron models, NeuronUnit, enables the construction of validation tests-executable functions that validate models against a single empirical observation to produce a score indicating agreement between the model and an observation. NeuronUnit facilitates the construction and logical grouping of tests for neuron models, the parameterization of tests using a wide range of empirical data, and the execution of tests against models in a continuous and transparent fashion. Aggregate results provide both theoretical and experimental neuroscientists with an overview of model suitability for targeted research questions. Merits and deficiencies of competing models are clearly visible, benefiting ongoing modeling efforts and informing new theoretical and experimental directions. This proposal aims to expand NeuronUnit to create data-driven, neuron-type-specific validation tests for reduced models. The ability of a range of reduced models to capture the relevant membrane potential and spiking dynamics of specific biological neuron types in response to specific stimuli, using publicly available experimental data from numerous sources, will be quantitatively tested and visualized. In doing so, the merits and deficiencies of each reduced model-as well as tradeoffs in model complexity, speed, and analysis-become transparent, providing critical information for model choice. Project aims are to 1) express a large number of reduced models using NeuroML/LEMS, 2) implement NeuronUnit testing of these models against data from a wide range of neuron- and experiment-types, 3) provide web-based search and visualization for test results and corresponding simulations, and 4) make these models available both as NeuroML documents and as code for every NeuroML-supported simulator. Collaborations with multiple existing initiatives will promote uptake of these tools, which for the first time, will provide a rigorous, transparent process for evaluation and selection of reduced models to address scientific questions about neurons. This project goes beyond model sharing by facilitating the dissemination of information about the performance and applicability of reduced neuron models in the context of specific datasets, complementing the existing dissemination mode of manuscript publication. By making model choice more deliberate and model appropriateness more objective, this work highlights which models should be used to address which scientific questions and why, without the need for a deep literature search (for models and data) or the installation of new tools or re-coding of models for simulation. The project also serves neuroscience educators by providing an interactive platform for visualization of reduced model dynamics accessible to any student, using data from biological neurons. This work broadly transforms theoretical neuroscience: by giving modelers a tool to select models quickly and with clear purpose; by rigorously identifying the models best-suited for further research efforts; and by helping experimentalists enhance the impact of their work.

 描述(申请人提供)：理论神经学家使用神经元模型来预测、理解和解释生物神经元行为。他们经常使用“简化的”神经元模型，这些模型抽象出生物细节，但捕捉到了基本的神经元动力学。这种选择便于数学处理、概念分析和计算速度。然而，使用这样的模型(而不是生物学上详细的模型)所固有的权衡是不透明的。人们通常不清楚一个模型是否忠实于神经元的基本观察动态，如果是的话，在什么模型参数和刺激条件下。在这方面，比较多种类型的简化模型的情况也很少见，这使得很难为科学问题选择最合适的一个。最后，这种模型一旦开发和参数化，通常不会以便于重现和重复使用的形式在研究人员之间共享，也不容易被发现。因此，在使用简化模型时的现状行为往往是简单地选择一个“最喜欢的”模型，而不考虑优点，针对手头的科学问题对其进行优化，然后将其丢弃。 专业软件开发中的标准实践是单元测试。单元测试是根据单个正确性标准来验证计算机程序的单个组件的过程。为神经元模型开发类似的单元测试程序NeuronUnit的工作正在进行中，它使得验证测试的构建成为可能--验证测试是可执行的函数，它针对单一的经验观察来验证模型，以产生一个分数，表明模型和观察之间的一致性。NeuronUnit有助于神经元模型测试的构建和逻辑分组，使用广泛的经验数据进行测试的参数化，以及以连续和透明的方式执行针对模型的测试。汇总的结果为理论和实验神经科学家提供了模型是否适合有针对性的研究问题的概述。竞争模型的优点和不足显而易见，有利于正在进行的建模工作，并提供新的理论和实验方向。 该提案旨在扩展NeuronUnit，为简化模型创建数据驱动的、特定于神经元类型的验证测试。将使用来自许多来源的公开可获得的实验数据，对一系列简化模型捕捉特定生物神经元类型对特定刺激的相关膜电位和尖峰动力学的能力进行定量测试和可视化。通过这样做，每个简化模型的优点和不足-以及模型复杂性、速度和分析方面的权衡-变得透明，为模型选择提供关键信息。 该项目的目标是1)使用NeuroML/LEMS表示大量简化的模型，2)根据来自各种神经元和实验类型的数据对这些模型进行NeuronUnit测试，3)为测试结果和相应的模拟提供基于Web的搜索和可视化，4)使这些模型既可以作为NeuroML文档使用，也可以作为每个NeuroML支持的模拟器的代码使用。与多个现有倡议的合作将促进这些工具的采用，这将首次为评估和选择简化模型提供一个严格、透明的过程，以解决有关神经元的科学问题。 该项目超越了模型共享，促进了关于简化神经元模型在特定数据集背景下的性能和适用性的信息的传播，补充了现有的手稿出版传播模式。通过使模型选择更加慎重，模型适当性更加客观，这项工作突出了哪些模型应该用来解决哪些科学问题和为什么，而不需要深入的文献搜索(模型和数据)，也不需要安装新的工具或重新编码用于模拟的模型。该项目还为神经科学教育工作者提供了一个互动平台，使用来自生物神经元的数据，对任何学生都可以访问的简化模型动力学进行可视化。这项工作广泛地改变了理论神经科学：通过为建模者提供一个工具来快速且有明确的目的地选择模型；通过严格地确定最适合于进一步研究的模型；通过帮助实验者提高他们工作的影响。