the correctness and performance of NTW by comparing it to a sequential deterministic simulation in NEURON. The derivation of a discrete event calcium wave model using the stochastic IP 3 R structure is one of my main scientific contributions of this thesis. This newly derived stochastic discrete event calcium wave model is described in chapter-5. In high performance issue, a dynamic load balancing algorithm and a dynamic window control algorithm for NTW was also developed for the stochastic calcium wave model. We use Q-learning to determine the basic control parameters of the algorithm. Prof. Tropper motivated me to employ Q-learning to optimize the performance of NTW in calcium wave simulation. Developing such an intelligent dynamic load balancing algorithm with dynamic window control is another research contribution of this thesis. All algorithms are described in chapter 6.
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ABSTRACTThe intra-cellular calcium signaling pathways of a neuron depends on both biochemical reactions and diffusions. Some quasi-isolated compartments (e.g. spines) are so small and the calcium concentrations are so low that one molecule diffusing into the compartment can make a nontrivial difference in calcium concentration. Such events can affect dynamics discretely in such a way that they cannot be evaluated by a deterministic simulation. Stochastic models of such a system provide a more detailed understanding of these systems than existing deterministic models because they capture their behavior at a molecular level. My research focuses on the development of a high performance parallel discrete event simulation environment, NTW , which is intended for use in the parallel simulation of stochastic reaction-diffusion systems such as intra-cellular calcium signaling. We make use of two models, a calcium buffer model and a calcium wave model. The calcium buffer model is employed in order to verify the correctness and performance of NTW by comparing it to a sequential deterministic simulation in NEURON. NEURON is a framework for simulating neurons which is used by neuroscientists world-wide. Our work is part of the NEURON project and has the long term goal of developing a multiscale simulation for large scale neuronal networks. We derived a discrete event calcium wave model from a deterministic model using the stochastic inositol 1,4,5-trisphosphate receptors, IP 3 R, structure. A dynamic load balancing algorithm and a dynamic window control algorithm for NTW was also developed for the stochastic calcium wave model.We make use of Q-learning to determine the basic control parameters of the algorithm.