NEURON SIMULATION ENVIRONMENT

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 我想在Cray XT 3上使用NEURON。我在过去的一年里一直这样做，但显然我在那台机器上的登录已经过期。我想回去继续进行维护，并测试单个单元的并行计算方法的性能。以下摘要已提交给在多伦多举行的CNS 2007会议：单神经元的全隐式并行模拟Michael Hines，Felix Schuermann，耶鲁大学计算机科学系，纽黑文，CT，06520，美国脑思维研究所，EPFL，洛桑，瑞士电子邮件：michael. yale.edu当树拓扑矩阵被划分为子树，其中每个子树在不同的CPU上，并且具有约束条件是其它子树在多于两个不同点（在该子树上定义主干路径）处不连接到给定子树，整个系统仍然服从直接高斯消去。由于在三角化阶段期间必须将三对角主干变换成N拓扑矩阵，所以复杂度增加是沿着主干沿着通常所需的除法次数的两倍和乘法次数的四倍。此外，每个子树需要将其根对角线和右手边元素，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手边发送到其中一个CPU，在那里将该信息加在一起以形成秩等于单元上的分裂点的数量的简化树矩阵。简化的树矩阵方程被求解，给出分裂点处的电压，并且该信息被发送回其他CPU上的适当子树。然后，那些具有主干的子树可以使用N拓扑来快速计算主干沿着的电压，每个人都可以完成其高斯消除的反向替换阶段。准确性与单个cpu上的标准高斯消去法相同，任何数量上的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，通常可以将一个三维重建的神经元模型分成十几个片段，并体验几乎线性的加速。我们使用的方法负载平衡的目的，在网络模拟时，一些细胞是非常大的比平均细胞和有更多的CPU比细胞。该方法在NEURON的当前标准分布中可用。鸣谢：NINDS资助NS 11613和瑞士联邦洛桑理工学院（EPFL）大脑研究所。

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: michael.hines@yale.edu 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.