NEURON SIMULATION ENVIRONMENT

神经元模拟环境

• 批准号: 7723237
• 负责人: MICHAEL L HINES
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723237
• 关键词: 3-Dimensional; Back; Brain; Cells; Computer Retrieval of Information on Scientific Projects Database; Electronic Mail; Elements; End Point; Environment; Equation; Equilibrium; Funding; Grant; Hand; Individual; Institutes; Institution; Maintenance; Methods; Mind; Modeling; Neurons; Numbers; Phase; Plant Roots; Purpose; Research; Research Personnel; Resources; Side; Source; Standards of Weights and Measures; Switzerland; System; Time; Trees; United States National Institutes of Health; Universities; Vertebral column; abstracting; computer science; experience; parallel computing; performance tests; simulation; voltage

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. I want to maintain NEURON for use on the Cray XT3. I was doing this for the past year but apparently my login has expired on that machine. I want to get back on to continue this maintenance and also to test the performance of a method for parallel computing of individual cells. The following abstract has been submitted to the CNS 2007 meeting in Toronto: Fully Implicit Parallel Simulation of Single Neurons Michael Hines, Felix Schuermann Department of Computer Science, Yale University, New Haven, CT, 06520, USA Brain Mind Institute, EPFL, Lausanne, Switzerland Email: [e-mail] When tree topology matrices are divided into subtrees where each subtree is on a different cpu and with the constraint that other subtrees are not connected to a given subtree at more than two distinct points (defining a backbone path on that subtree), the entire system remains amenable to direct gaussian elimination. The complexity increase is twice the number of divisions and four times the number of multiplications normally required along the backbones due to the necessity, during the triangularization phase, of transforming the tridiagonal backbone into an N topology matrix. In addition, each subtree is required to send its root diagonal and right hand side element, or, in the case of a subtree with a backbone, the 2x2 matrix and right hand sides of the backbone end points, to one of the cpus where that information is added together to form a reduced tree matrix of rank equal to the number of split points on the cell. The reduced tree matrix equation is solved, giving the voltages at the split points, and this information is sent back to the appropriate subtrees on the other cpus. Those subtrees with backbones can then use the N topology to quickly compute the voltages along the backbone and everyone can complete the back substitution phase of their gaussian elimination. Accuracy is the same as with standard gaussian elimination on a single cpu and any quantitative differences are attributed to accumulated round off error due to different ordering of subtrees containing backbones. With this method, it is often feasible to divide a 3-d reconstructed neuron model into a dozen or so pieces and experience almost linear speedup. We have used the method for purposes of load balance in network simulations when some cells are very much larger than the average cell and there are more cpus than cells. The method is available in the current standard distribution of NEURON. Acknowledgments: NINDS grant NS11613 and the Brain Mind Institute, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 我想保留 NEURON 以在 Cray XT3 上使用。我去年就这样做了，但显然我的登录信息已经在那台机器上过期了。我想回去继续进行维护，并测试单个单元并行计算方法的性能。以下摘要已提交至多伦多 CNS 2007 会议： 单神经元的完全隐式并行模拟 Michael Hines, Felix Schuermann Department of Computer Science, Yale University, New Haven, CT, 06520, USA Brain Mind Institute, EPFL, Lausanne, Switzerland 电子邮件：[e-mail] 当树拓扑矩阵被划分为子树时，每个子树位于不同的子树上 cpu 并且由于其他子树未在两个以上不同点连接到给定子树（在该子树上定义主干路径）的约束，整个系统仍然适合直接高斯消除。由于在三角化阶段需要将三对角主干转换为 N 拓扑矩阵，因此复杂性的增加是主干上通常所需的除法次数的两倍和乘法次数的四倍。此外，每个子树都需要将其根对角线和右侧元素，或者在具有主干的子树的情况下，将 2x2 矩阵和主干端点的右侧发送到其中一个 cpu，在该 cpu 中，该信息被添加在一起以形成等级等于单元上分割点数量的简化树矩阵。求解简化的树矩阵方程，给出分割点处的电压，并将该信息发送回其他 cpu 上的相应子树。然后，那些具有主干的子树可以使用 N 拓扑快速计算主干上的电压，并且每个人都可以完成高斯消除的回代阶段。准确性与单个 CPU 上的标准高斯消除相同，任何数量差异都归因于由于包含主干的子树的不同排序而导致的累积舍入误差。使用这种方法，通常可以将 3D 重建神经元模型划分为十几个左右的部分，并获得几乎线性的加速。当某些单元比平均单元大得多并且 cpu 数量多于单元时，我们在网络模拟中使用该方法来实现负载平衡。该方法在当前的 NEURON 标准发行版中可用。致谢：NINDS 授予 NS11613 和瑞士洛桑联邦理工学院 (EPFL) 大脑思维研究所。