Parallel Simulation of Neurons

我的长期目标是为计算上具有挑战性的重大问题开发并行离散模型，因为（1）离散事件模型便于详细建模，（2）可以并行执行。 我建议，这个DG，一个神经元的化学和电气模型之间的相互作用的并行离散事件模拟。该项目将与Downstate医学院（SUNY）的神经科学家Lytton博士和耶鲁大学的神经科学家Hines博士合作完成。在计算神经科学中广泛使用的模拟器NEURON将利用这项研究。这项研究的好处是（1）增强对神经元的理解，这可以导致神经系统疾病的治疗（2）可扩展的混合离散事件模拟的发展取得进展。神经元的细胞膜是半渗透性的，其中有控制细胞体内外离子流动的通道。详细的电学模型，基于众所周知的电学定律，描述了神经元的许多特性。然而，这些模型背后的假设，即细胞内和细胞外离子浓度是恒定的，实际上被钙离子所破坏，钙离子在细胞内信号传导中起着重要作用。因此，有必要对离子浓度进行建模，以便正确地对神经元的电响应进行建模。关键的计算挑战是事件处理的细粒度性质和维护非常大的状态历史的需要。我希望利用我的小组在动态负载平衡算法和人工智能方法的应用方面所做的广泛研究来控制乐观模拟（窗口大小的计算，动态负载平衡）。接下来将进行电学（连续）和化学（离散事件）模型的整合。我们将继续研究恒星系统的n体问题，利用乐观模拟沿着碰撞检测算法来研究碰撞产生的物体（碎石堆）问题，这些物体通过引力结合在一起。好处包括更好地了解行星的形成和发展的可扩展算法的n体问题。

My long term goal is to develop parallel discrete models for computationally challenging, significant problems because (1)discrete event models facilitate detailed modeling and (2) can be executed in parallel. I am proposing, for this DG, the parallel discrete event simulation of the interaction between chemical and electrical models of a neuron. The project will be done in collaboration with Dr. Lytton, a neuroscientist at the Downstate Medical School (SUNY) and Dr. Hines, a neuroscientist at Yale University. NEURON, a widely used simulator in computational neuroscience will make use of this research. Benefits of this research are (1) an enhanced understanding of the neuron which can lead to treatments for neurological disorders (2) progress in development of scalable hybrid discrete event simulations. The membrane of a neuron is semi-permeable and has channels within it which control the flow of ions between the exterior and the interior of the cell body.Detailed electrical models, based upon well-known electrical laws describe many of the properties of a neuron. However, an assumption behind these models, that intracellular and extracellular ionic concentrations are constant is actually violated by calcium ions, which play a significant role in intracellular signaling. Hence it is necessary to model ionic concentrations in order to correctly model the electrical response of a neuron. Key computational challenges are the fine-grained nature of event processing and the need to maintain a very large state history. I expect to leverage the extensive research done in my group on dynamic load balancing algorithms and on the application of AI methods to control optimistic simulation (computation of window size, dynamic load balancing). The subsequent integration of the electrical (continuous) and chemical models (discrete event) will follow.We will continue our work on the n-body problem for stellar systems, making use of optimistic simulation along with collision detection algorithms to investigate the problem of bodies (rubble piles) which result from collisions and which are held together by gravity. Benefits include a better understanding of planetary formation and the development of scalable algorithms for the n-body problem.