Network Dynamics Modeling Analysis (3 results)

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    • Language: English

      Published by VDM Verlag Dr. Müller, 2009

      3639157532 / 9783639157536

      • Softcover

      Seller: preigu, Osnabrück, Germanypreigu

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      Taschenbuch. Condition: Neu. Population Density Approach to Neural Network Modeling | Dimension Reduction Analysis, Techniques, and Firing Rate Dynamics | Cheng Ly | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639157536 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

    • Language: Chinese

      Published by Science Press, 2000

      7030409043 / 9787030409041

      • Softcover

      Seller: liu xing, Nanjing, JS, Chinaliu xing

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      paperback. Condition: New. Language:Chinese.Network Dynamics Modeling and Analysis of Infectious Diseases.

    • Language: English

      Published by VDM Verlag Dr. Müller, 2009

      3639157532 / 9783639157536

      • Softcover
      • Print on Demand

      Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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      Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Population Density Methods (PDM) have gainedprominence in recent years in Theoretical Neuroscience as an analyticaland time-saving computational tool. The method involves solving adensity equation (aka, Fokker-Planck) instead of simulating manyindividual neurons. Simplifying assumptions of the underlying neuronmodel are often made so that the resulting PDM equations have lowdimension for tractability. Thus, dimension reduction techniquesare vital for physiological modeling. An introduction to PDM and the relevant issues are discussed in Chapter 2. A''moment closure'' dimension reduction technique is analyzed in Chapter3. We show the equations are ill-posed in the fluctuation-driven regime withrealistic parameters despite several contrary reports in the literature. The dimension reduction method is even worse for the more physiological''theta'' model (Chapter 4). A robust and accurate alternativereduction technique using a moving eigenvector basis is developed andimplemented in Chapter 5. The stochastic firing rate dynamics ofvarious neural models are analyzed in Chapter 6 with the tools wehave developed.