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.

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 我想维护Cray XT3上使用的神经元。我在过去的一年里一直在这样做，但显然我在那台机器上的登录已经过期。我想继续进行维护，并测试用于单个单元并行计算的方法的性能。以下摘要已提交给在多伦多举行的CNS2007会议：全隐式单神经元并行模拟Michael Hines，Felix Schuermann计算机科学系，耶鲁大学，纽黑文，CT，06520，美国脑心理研究所，EPFL，洛桑，瑞士电子邮件：[e-mail]当树拓扑矩阵被分成子树时，其中每个子树在不同的CPU上，并且在其他子树不在超过两个不同的点处连接到给定子树(在该子树上定义骨干路径)的约束下，整个系统仍然能够直接进行高斯消除。由于在三角化阶段需要将三对角线主干转换成N个拓扑矩阵，所以复杂性增加是沿着主干通常所需的除法数目的两倍和乘法数目的四倍。此外，每个子树被要求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CPU之一，在那里将该信息相加在一起以形成等同于单元上分裂点数的降阶树矩阵。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上的适当子树。然后，那些带有主干的子树可以使用N个拓扑结构来快速计算主干上的电压，每个人都可以完成其高斯消除的反向替换阶段。精度与单CPU上的标准高斯消去法相同，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，将一个三维重建的神经元模型分成十几个左右的片段并经历几乎线性的加速比通常是可行的。在网络模拟中，当一些小区比平均小区大得多，并且CPU比小区多时，我们使用该方法来实现负载平衡。该方法适用于当前神经元的标准分布。致谢：NINDS Grant NS11613和瑞士洛桑理工学院大脑研究所(EPFL)。