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Математика Математика
Основы математического анализа и элементы дифференциальных уравнений Основы математического анализа и элементы дифференциальных уравнений


Dimension Reduction Analysis, Techniques, and Firing Rate Dynamics Population Density Approach to Neural Network Modeling.

Автор:   Cheng L.
мягкая обложка
144 страниц
2009 год
Издательство:   Книга по Требованию
2382 руб
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Описание:The dimension reduction method is even worse for the more physiological ' theta' model (Chapter 4). A robust and accurate alternative reduction technique using a moving eigenvector basis is developed and implemented in Chapter 5. The stochastic firing rate dynamics of various neural models are analyzed in Chapter 6 with the tools we have developed. Simplifying assumptions of the underlying neuron model are often made so that the resulting PDM equations have low dimension for tractability. Population Density Methods (PDM) have gained prominence in recent years in Theoretical Neuroscience as an analytical and time-saving computational tool. Thus, dimension reduction techniques are vital for physiological modeling. The method involves solving a density equation (aka, Fokker-Planck) instead of simulating many individual neurons. An introduction to PDM and the relevant issues are discussed in Chapter 2. A ' moment closure' dimension reduction technique is analyzed in Chapter 3. We show the equations are ill-posed in the fluctuation-driven regime with realistic parameters despite several contrary reports in the literature.

Orthomol Immun Junior Сила иммунитета на страже Вашего ребенка Orthomol Immun Junior Сила иммунитета на страже Вашего ребенка

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Учебное пособие по курсу «Нейроинформатика»

Liability for malfunction of an expert system // IEEE Conf. Manag. Expert Syst. Program and Proj., Bethesda, Md. Sept. 10-12, 1990: Proc.- Los Alamitos (Calif.) etc., 1990.- PP. 8-15. 278. Genis C. T. Relaxation and neural learning: points of convergence and divergence // J. Parallel and Distrib. Comput.- 1989.- 6, № 2.- PP. 217-244. 279. George N., Wang hen-ge, Venable D.L. Pattern recognition using the ring-wedge detector and neural-network software: [Pap.] Opt. Pattern Recogn. II: Proc. Meet., Paris, 26-27 Apr., 1989 // Proc. Soc. Photo-Opt. Instrum. Eng.- 1989.- PP. 96-106. 280. Gilev S.E. A non-back-propagation method for obtaining the gradients of estimate function // Advances in Modelling & Analysis, A, AMSE Press, 1995. Vol. 29, № 1. PP. 51-57. 281. Gilev S.E., Gorban A.N. On Completeness of the Class of Functions Computable by Neural Networks, Proc. of the World Congress on Neural Networks, Sept. 15-18, 1996, San Diego, CA, Lawrence Erlbaum Associates, 1996, pp. 984-991. 282. Gilev S.E., Gorban A.N., Kochenov D.A., Mirkes Ye.M., Golovenkin S.E., Dogadin S.A., Nozdrachev K.G., Maslennikova E.V., Matyushin G.V., Rossiev D.A., Shulman V.A., Savchenko A.A. "MultiNeuron" neural simulator and its medical applications // Proceedings of International Conference on Neural Information Processing, Oct. 17-20, 1994, Seoul, Korea.- V.2.- P.1261-1264. 283. Gilev S.E., Gorban A.N., Mirkes E.M ...»

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