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Wyniki 1-1 spośród 1 dla zapytania: authorDesc:"Agata Jurkowlaniec"

» Parallel simulation of stochastic denritic neurons using NVidia GPUs with CUDA

Karol Gugała

Aleksandra Świetlicka

Agata Jurkowlaniec

Andrzej Rybarczyk

Hodgkin-Huxley model has been experimentally proved over years great usefulness in describing action potentials of neural cells. Model explains initiation and propagation of action potentials in the giant squid axon, which is very useful in case studies on action potentials [9] in biological neural networks. For work in this matter Alan Hodgkin and Andrew Huxley receive Nobel prize in 1963. Main disadvantage of mentioned model is it computational complexity, thus high demand of computational power and time expensive simulations. We consider computational simplification of model by transformation from deterministic to stochastic one. This transformation simplifies model by replacement part of differential equations derived by it with draw from normal distribution. To decrease time of simulations we decide to use parallel computing. High number of simple floating point operations led us to use GPUs in simulation tasks. In this paper we present simple dendritic neurons in tree like structures tissues simulator based on NVidia GPU written in CUDA C. We present transformation from deterministic Hodgkin - Huxley model to stochastic one. Parallelisation of simulation task of dendritic neurons in aforementioned tissues is presented. Also implemented simulator parallel algorithm is discussed. Hodgkin-Huxley kinetic model Deterministic model is given with the set of equations, where the main equation is of the form (1). Other equations are obtained from the Markov kinetic schemes,[...] więcej»
w zeszycie ELEKTRONIKA - KONSTRUKCJE, TECHNOLOGIE, ZASTOSOWANIA 2011/12

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