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（由适用提供）：理论神经科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“减少”神经元模型，这些神经元模型抽象生物学细节，但捕获基本的神经元动力学。这种选择促进了数学障碍，概念分析和计算速度。但是，使用此类模型（而不是生物学上详细的模型）所固有的权衡是尚不清楚模型是否忠于神经元的基本动力学，如果是的，则在哪种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也很少见，因此很难为科学问题选择最合适的模型。最后，这样的模型一旦开发和参数化，通常不会以促进可重复性和重复使用的形式共享研究人员，也不能轻易发现它们。结果，使用简化模型中的现状行为通常只是选择一个“最喜欢的”模型，无论其优点如何，都可以优化它作为手头的科学问题，然后将其丢弃。 专业软件开发的标准实践是单元测试。单位测试是一个程序，可根据单个正确性标准验证计算机程序的单个组件。为神经元模型开发类似的单位测试程序的持续努力，神经元素能够构建验证测试 - 执行的函数，该功能可验证模型，从而针对单个经验观察，以产生模型与观察结果之间一致的分数。 Neuronunit促进了神经元模型测试的构建和逻辑组，使用广泛的经验数据进行测试的参数化以及以连续且透明的方式对模型的测试执行。骨料结果提供了理论和实验神经科学家，并概述了针对目标研究问题的模型适用性。竞争模型的优点和缺陷清晰可见，从而使持续的建模工作受益并为新的理论和实验方向提供信息。 该建议旨在扩展神经元素，以创建数据驱动的，神经型特异性验证测试，以减少模型。使用来自众多来源的公开可用的实验数据，将对特定的生物神经元类型捕获特定生物神经元类型的相关膜电位和尖峰动态的能力将进行定量测试和可视化。这样一来，每个降低的模型的优点和缺陷 - 以及模型复杂性，速度和分析透明的折衷方案，为模型选择提供关键信息。 项目的目的是1）使用神经/lems表达大量减少模型，2）对这些模型的神经持有测试对来自广泛的神经和实验类型的数据实施神经量测试，3）提供基于Web的搜索和可视化测试结果和相应的模拟，以及4）使这些模型均为Neuroml文档和每个Neuroml simeRET simul for aunuroml simul and aS aS aS aS aS aS aS aS simul simul。与多个现有计划的合作将促进这些工具的吸收，这首先将为评估和选择简化模型的严格，透明的过程，以解决有关神经元的科学问题。 该项目通过支持在特定数据集中降低神经元模型的性能和适用性的信息传播，超越了模型共享，从而完成了手稿出版物的现有传播模式。通过使模型选择更加故意和模型适当性，这项工作强调了哪些模型应用于解决哪些科学问题以及为什么不需要深入文献搜索（用于模型和数据）或安装新工具或重新编码模型进行模拟。该项目还通过提供一个使用生物神经元的数据来可视化任何学生访问的减少模型动态的交互式平台，为神经科学教育者提供服务。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种快速和清晰目的选择模型的工具；通过严格确定最适合进一步研究工作的模型；并通过帮助实验者增强其工作的影响。