Dissemination of a tool for data-driven multiscale modeling of brain circuits

传播数据驱动的脑回路多尺度建模工具

• 批准号: 10827627
• 负责人: Salvador Dura-Bernal
• 金额: $ 21.21万
• 依托单位: SUNY DOWNSTATE MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-18 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10827627
• 关键词: Acceleration; Address; Adoption; Algorithms; Architecture; Auditory; Award; Benchmarking; Biomedical Research; Biophysics; Brain; Brain imaging; Characteristics; Chemicals; Collaborations; Communities; Complex; Computer Models; Cost efficiency; Data; Development; Disease; Electric Stimulation; Ensure; Environment; Funding; Goals; Individual; Institution; Measures; Memory; Methods; Modeling; Modernization; Neurons; Neurosciences; Neurosciences Research; Output; Parents; Peer Review; Performance; Pharmacology; Process; Provider; Publications; Publishing; Research; Research Personnel; Research Project Grants; Resources; Running; Software Tools; Solid; Speed; Students; Synapses; Technology; Testing; Time; Underrepresented Populations; United States National Institutes of Health; Work; Workload; base; biophysical analysis; biophysical model; biophysical properties; cloud based; computational neuroscience; computing resources; cost effective; data tools; experience; experimental study; improved; in silico; interoperability; large scale simulation; multi-scale modeling; neural; neuronal circuitry; open source; parallel computer; parent grant; response; simulation; software development; somatosensory; tool; virtual

Project Summary Experiments aimed at discovering how the brain works generate vast amounts of data that span multiple scales: from interactions between individual molecules to waves of electrical activity across the entire brain. Computational modeling provides a way to integrate and make sense of these data. Through the parent grant U24EB028998 we are developing and disseminating NetPyNE, a tool for data-driven multiscale modeling of brain circuits. This tool provides a programmatic and graphical high-level interface to the widely-used NEURON simulator that facilitates the development, parallel simulation, optimization and analysis of biophysically detailed neuronal circuits. NetPyNE uses CoreNEURON, an improved simulation engine optimized for parallel simulation on both CPUs and GPUs. Significant progress has been made towards achieving the parent grant goal of transforming NetPyNE into a solid and well-tested tool with a fully-featured GUI, and widely disseminating the tool among the scientific community. This is supported by a growing user base, as evidenced by over 100 models being developed across more than 40 institutions worldwide, over 30 peer-reviewed publications making use of the tool. NetPyNE has also been integrated or interfaced with multiple community standards, tools and platforms, including the NeuroML and SONATA, the Open Source Brain, EBRAINS and The Neuroscience Gateway (NSG), HNN, SciUnit/SciDash, LFPy, and The Virtual Brain. This supplement proposal aims to explore and evaluate the use of cloud-based GPU resources to accelerate large-scale biophysically-detailed simulations of brain circuits using NetPyNE and the CoreNERON simulation engine. CoreNEURON focuses on improving performance by modernizing the legacy NEURON simulation engine to be optimized for parallel computation on modern architectures, including cloud GPUs. The yield of offloading these computationally intensive tasks from CPUs to GPUs has been demonstrated on several in-silico models with speedups of up to 40x. Currently, performance increases have only been implemented and evaluated for a handful of models. To facilitate the adoption of GPU utilization for large-scale modeling of brain circuits, we will evaluate the recently published NetPyNE-based somatosensory (S1) and auditory (A1) thalamocortical large-scale models on cloud GPU resources. We will first evaluate individual simulations on a single GPU node (Aim 1). Next, we will evaluate, for the first time, the use of clusters of GPU nodes to perform large parameter optimizations by running many large-scale simulations simultaneously (Aim 2). We will apply rigorous benchmarking measures, including computation time and memory usage, to evaluate the feasibility of this approach and characterize its benefits across use cases, including models of different sizes and different cloud configurations. This supplement will enhance the performance, interoperability and community adoption of NetPyNE, accelerate multiple NIH-funded research projects that use NetPyNE, and make cloud GPU technologies more accessible to under-resourced institutions and communities.

项目摘要 旨在发现大脑如何工作的实验产生了大量跨越多个尺度的数据： 从单个分子之间的相互作用到整个大脑的电活动波。计算 建模提供了一种集成和理解这些数据的方法。通过母基金U24 EB 028998，我们 开发和传播NetPyNE，这是一种用于大脑电路数据驱动多尺度建模的工具。此工具提供 广泛使用的NEURON模拟器的编程和图形高级界面， 开发、并行模拟、优化和分析生物解剖学详细的神经元回路。NetPyNE使用 CoreNEURON，一个改进的模拟引擎，针对CPU和GPU上的并行模拟进行了优化。显著 在实现将NetPyNE转变为一个坚实且经过良好考验的母公司赠款目标方面取得了进展 该工具具有功能齐全的GUI，并在科学界广泛传播该工具。这是由一个 不断增长的用户群，全球40多个机构正在开发100多个模型， 30多份同行评审出版物使用了该工具。NetPyNE还集成或与 多种社区标准、工具和平台，包括NeuroML和SONATA，开源大脑， EBRAINS和The Neuroscience Gateway（NSG）、HNN、SciUnit/SciDash、LFPy和The Virtual Brain。 本补充提案旨在探索和评估使用基于云计算的GPU资源来加速 使用NetPyNE和CoreNERON模拟引擎对大脑回路进行大规模生物药理学详细模拟。 CoreNEURON专注于通过现代化传统NEURON仿真引擎来提高性能， 针对现代架构（包括云GPU）上的并行计算进行了优化。卸载这些的收益 从CPU到GPU的计算密集型任务已经在几个计算机模型上得到了演示， 加速高达40倍。目前，只有少数几个国家实施和评估了业绩提高， 模型为了便于采用GPU利用率进行大规模脑回路建模，我们将评估 最近发表的基于NetPyNE的体感（S1）和听觉（A1）丘脑皮层大规模模型， 云GPU资源。我们将首先评估单个GPU节点上的各个模拟（目标1）。接下来我们就 首次评估使用GPU节点集群执行大参数优化， 同时进行多个大规模模拟（目标2）。我们会采取严格的基准措施，包括 计算时间和内存使用情况，以评估这种方法的可行性，并描述其在 使用案例，包括不同规模和不同云配置的模型。这一补充将加强 NetPyNE的性能，互操作性和社区采用，加速多个NIH资助的研究项目 使用NetPyNE，使云GPU技术更容易为资源不足的机构所用， 社区.

项目成果

The Roles of Potassium and Calcium Currents in the Bistable Firing Transition.

• DOI: 10.3390/brainsci13091347
• 发表时间: 2023-09-20
• 期刊: Brain sciences
• 影响因子: 3.3

Theta-gamma phase amplitude coupling in a hippocampal CA1 microcircuit.

• DOI: 10.1371/journal.pcbi.1010942
• 发表时间: 2023-03
• 期刊: PLoS computational biology
• 影响因子: 4.3

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE.

Netpyne的体感丘脑皮层电路的大规模生物物理详细模型。

• DOI: 10.3389/fninf.2022.884245
• 发表时间: 2022
• 期刊: FRONTIERS IN NEUROINFORMATICS
• 影响因子: 3.5
• 作者: Borges, Fernando S.;Moreira, Joao V. S.;Takarabe, Lavinia M.;Lytton, William W.;Dura-Bernal, Salvador
• 通讯作者: Dura-Bernal, Salvador