Computer Methods for Physiological Problems

描述（由申请人提供）：该项目的主要目标是增强NEURON的并行能力;这是由研究人员开始提出和研究的健康和疾病中大脑功能的大规模生物物理学现实模型推动的。NEURON是一个神经元和神经回路的开源模拟器，我们为OS X、MS Windows和UNIX/Linux开发并免费提供。它简化了数值方法和编程方面的非专家的神经模型的创建和分析。NEURON在工作站、集群和大规模并行超级计算机上运行，模型属性可能包括但不限于复杂的分支形态、多通道类型、不均匀的通道分布、离子扩散、细胞外场、电子仪器和人工尖峰细胞。 越来越多的神经科学家在世界各地的实验室正在使用它来研究各种疾病的正常和异常脑功能的机制，例如癫痫，多发性硬化症和学习和记忆障碍，以及这些如何受到治疗干预的影响，如药物和脑深部电刺激（DBS）。他们正在解决的问题涉及到越来越大的解剖和生物物理复杂性，这使得执行速度更加重要，但硬件串行执行性能在五年多的时间里一直保持不变。 神经元已经支持线程和MPI风格的并行尖峰耦合网络，间隙连接，和分布式电缆模型的个别细胞，但我们已经确定了三个领域，大的改进仍然是可以实现的。首先是GPU并行化，这将允许具有复杂膜特性的程式化细胞的网络模型实现超过一个数量级的速度提升。第二个是NEURON的微分代数方程求解器的并行化，这将使涉及细胞外场，反应扩散和细胞间相互作用的模型能够利用多核和并行集群架构。第三个是支持模型设置的多线程并行化。帮助 评估和完善这些新的功能，我们将实现一个模型的DBS，许多实验和计算研究的焦点，因为它的证明和潜在的临床效用。 该项目的传播和支持部分包括：通过NeuroML 2增强NEURON与其他模拟器的互操作性;软件维护，以确保用户能够继续在OS X、MS Windows、UNIX/Linux的所有组合下运行NEURON（有或没有Python），在当前的32位和64位处理器架构上仍然使用各种版本的Python;通过电子邮件和NEURON论坛积极支持用户;创建新的文档并发表文章和报告;介绍课程并组织NEURON用户会议。

DESCRIPTION (provided by applicant): The primary goal of this project is to enhance the parallel capabilities of NEURON; this is motivated by the large scale of biophysically realistic models of brain function in health and disease that investigators are beginning to propose and study. NEURON is an open source simulator of neurons and neural circuits that we have developed and provide freely for OS X, MS Windows, and UNIX/Linux. It simplifies the creation and analysis of neural models for nonspecialists in numerical methods and programming. NEURON runs on workstations, clusters, and massively parallel supercomputers, and model properties may include, but are not limited to, complex branching morphology, multiple channel types, inhomogeneous channel distribution, ionic diffusion, extracellular fields, electronic instrumentation, and artificial spiking cells. Growing numbers of neuroscientists in laboratories around the world are using it to investigate mechanisms that underlie normal and abnormal brain function in a wide range of disorders, e.g. epilepsy, multiple sclerosis, and disorders of learning and memory, as well as how these are affected by therapeutic interventions such as medications and deep brain stimulation (DBS). The problems they are addressing involve ever greater anatomical and biophysical complexity, which puts a premium on execution speed, yet hardware serial execution performance has remained unchanged for more than half a decade. NEURON already supports thread and MPI styles of parallelism for spike coupled networks, gap junctions, and distributed cable models of individual cells, but we have identified three areas where large improvements are still achievable. The first is GPU parallelization, which will allow network models of stylized cells with complex membrane properties to achieve more than an order of magnitude speed increase. The second is parallelization of NEURON's differential-algebraic equation solver, which will enable models involving extracellular fields, reaction-diffusion, and ephaptic interactions between cells to take advantage of multicore and parallel cluster architectures. The third is to enable multithreaded parallelization of model setup. To help evaluate and refine these new features, we will implement a model of DBS, a focus of much experimental and computational research because of its demonstrated and potential clinical utility. The dissemination and support component of this project includes: enhancing NEURON's interoperability with other simulators via NeuroML 2; software maintenance to ensure that users continue to be able to run NEURON with or without Python under all combinations of OS X, MS Windows, UNIX/Linux, with the various versions of Python still in use, on current 32- and 64-bit processor architectures; active support of users through email and the NEURON Forum; creation of new documentation and publication of articles and reports; presenting courses and organizing meetings of NEURON users.