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会产生大量跨越多个尺度的数据：从 单个分子与整个大脑的电活动波之间的相互作用。计算 建模提供了一种集成和理解这些数据的方法。通过父母授予U24EB028998我们是 开发和传播Netpyne，这是数据驱动的大脑电路多尺度建模的工具。此工具提供 广泛使用的神经元模拟器的编程和图形高级接口，可帮助该神经元模拟器 生物物理详细的神经元电路的开发，平行模拟，优化和分析。 Netpyne使用 Coreneuron，一种改进的模拟引擎，优化了CPU和GPU的平行模拟。重要的 取得了进步，取得了将Netpyne转变为坚实且经过充分测试的父母赠款目标 具有功能齐全的GUI的工具，并在科学界广泛传播该工具。这是由 在全球40多家机构中开发了100多个模型证明的用户基础，这证明了用户群 超过30多个同行评审的出版物使用该工具。 Netpyne也已集成或与 多个社区标准，工具和平台，包括神经元和奏鸣曲，开源大脑， Ebrains和Neuroscience Gateway（NSG），HNN，Sciunit/Scidash，LFPY和虚拟大脑。 该补充建议旨在探索和评估基于云的GPU资源的使用来加速 使用Netpyne和Coreneron仿真引擎对脑电路进行大规模生物物理测定的模拟。 Coreneuron专注于通过现代化传统神经元模拟引擎的现代化来提高性能 针对现代体系结构（包括云GPU）的现代体系结构的并行计算进行了优化。卸载这些的产量 从CPU到GPU的计算密集型任务已在几种具有的内部模型上证明 高达40倍的加速度。目前，仅实施和评估了少数的性能提高 型号。为了促进采用GPU利用用于大脑电路的大规模建模，我们将评估 最近出版的基于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节点上的单个模拟（AIM 1）。接下来，我们会的 首次评估GPU节点簇通过运行进行大型参数优化 许多大规模模拟同时模拟（AIM 2）。我们将采取严格的基准测试措施，包括 计算时间和内存使用量，以评估这种方法的可行性，并在各个方面表征其好处 用例，包括不同尺寸和不同云配置的模型。这种补充将增强 Netpyne的绩效，互操作性和社区采用，加速了多个NIH资助的研究项目 使用Netpyne，使云GPU技术更容易被资源不足的机构访问 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.

• 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