Random number generation system improving simulations of stochastic models of neural cells

• DOI: 10.1007/s00607-012-0267-z
• Publication Date: 2013-01-02T17:34:38Z
• Authors: Karol Gugała; Aleksandra Świetl...; Michał Burdajewicz; Andrzej Rybarczyk
• Keywords: Numerical Analysis; Computational Mathematics; 0209 industrial biotechnology; Computational Theory and Mathematics; 02 engineering and technology; Software; Theoretical Computer Science; Computer Science Applications

The purpose of this work is to speed up simulations of neural tissues based on the stochastic version of the Hodgkin–Huxley model. Authors achieve that by introducing the system providing random values with desired distribution in simulation process. System consists of two parts. The first one is a high entropy fast parallel random number generator consisting of a hardware true random number generator and graphics processing unit implementation of pseudorandom generation algorithm. The second part of the system is Gaussian distribution approximation algorithm based on a set of generators of uniform distribution. Authors present hardware implementation details of the system, test results of the mentioned parts separately and of the whole system in neural cell simulation task.