NEURON SIMULATION ENVIRONMENT

神经元模拟环境

• 批准号: 7601500
• 负责人: MICHAEL L HINES
• 金额: $ 0.03万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7601500
• 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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 我想维护在cray XT3上使用的神经元。在过去的一年中，我一直在这样做，但显然我的登录已在那台机器上到期。我想重新继续进行此维护，并测试单个单元单元平行计算方法的性能。以下摘要已提交在多伦多举行的CNS 2007会议上：完全隐含的平行模拟迈克尔·海因斯（Michael Hines），菲利克斯·舒尔曼（Felix Schuermann CPU并限制了其他子树在超过两个不同的点（定义该子树上的骨干路径）上没有连接到给定的子树，整个系统仍然可以直接直接高斯消除。复杂性的增加是划分数量的两倍，是在三角化阶段的必要性，将三角形主链转换为N拓扑矩阵，这是沿骨干通常所需的乘法数量的四倍。此外，每个子树都必须将其根对角线和右侧元素发送，或者，在带有主链的子树的情况下，2x2矩阵和骨干端点的2x2矩阵和右侧侧面，将信息添加在一起以形成一个与单元格上的划分点相等的级别级别的树矩阵。还求解了还原的树矩阵方程，从而给出了拆分点的电压，并将此信息发送回另一个CPU上的适当子树。然后，那些带有骨架的子树可以使用N拓扑来快速计算骨干沿线的电压，每个人都可以完成高斯消除的背部替代阶段。准确性与单个CPU上的标准高斯消除相同，任何定量差异都归因于由于包含骨架的子树的不同排序而导致的圆形误差。通过这种方法，通常可以将3D重建的神经元模型分为十几个片段，并经历几乎线性的加速，这通常是可行的。当某些细胞比平均单元格大得多，并且CPU比单元格的方法大得多时，我们已将方法用于网络模拟中的负载平衡。该方法在神经元的当前标准分布中可用。致谢：Ninds Grant NS11613和Brain Mind Institute，Ecole Polytechnique Federale de Lausanne（EPFL），瑞士。