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Encyclopedia of Computational Neuroscience / edited by Dieter Jaeger, Ranu Jung.

Contributor(s): Jaeger, Dieter, editor | Jung, Ranu, editor
Series: (Biomedical and Life Sciences (Springer-11642)).Publisher: New York : Springer International Publishing, 2019Description: 1 recurso en línea (aproximadamente 3000 páginas) : 1000 ilustraciones.ISBN: 9781461473206.Subject: Neurociencia computacionalOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources Abstract: The annual Computational Neuroscience Meeting (CNS) began in 1990 as a small workshop called Analysis and Modeling of Neural Systems. The goal of the workshop was to explore the boundary between neuroscience and computation. Riding on the success of several seminal papers, physicists had made "Neural Networks" fashionable, and soon the quantitative methods used in these abstract model networks started permeating the methods and ideas of experimental neuroscientists. Although experimental neurophysiological approaches provided many advances, it became increasingly evident that mathematical and computational techniques would be required to achieve a comprehensive and quantitative understanding of neural system function. "Computational Neuroscience" emerged to complement experimental neurophysiology. In 2002, the non-profit organization, Organization for Computational Neuroscience (OCNS) was formed. OCNS has now become the first professional society serving the global computational neuroscience community. OCNS as a society lives at the interface where experimental neuroscience meets theoretical, statistical and computer-simulation analyses, with the hope of turning large collections of experimental results into a principled understanding of nervous systems. It also supports the development of new engineering, computational and informatics techniques for data collection, analyses and management.   The Encyclopedia of Computational Neuroscience will be consultable by both researchers and graduate level students. It will be a dynamic, living reference, continually updatable and containing linkouts and multimedia content whenever relevant.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias de la Salud QP357.5 2019 EB (Browse shelf(Opens below)) Acceso electrónico eBook05112036
Total holds: 0

The annual Computational Neuroscience Meeting (CNS) began in 1990 as a small workshop called Analysis and Modeling of Neural Systems. The goal of the workshop was to explore the boundary between neuroscience and computation. Riding on the success of several seminal papers, physicists had made "Neural Networks" fashionable, and soon the quantitative methods used in these abstract model networks started permeating the methods and ideas of experimental neuroscientists. Although experimental neurophysiological approaches provided many advances, it became increasingly evident that mathematical and computational techniques would be required to achieve a comprehensive and quantitative understanding of neural system function. "Computational Neuroscience" emerged to complement experimental neurophysiology. In 2002, the non-profit organization, Organization for Computational Neuroscience (OCNS) was formed. OCNS has now become the first professional society serving the global computational neuroscience community. OCNS as a society lives at the interface where experimental neuroscience meets theoretical, statistical and computer-simulation analyses, with the hope of turning large collections of experimental results into a principled understanding of nervous systems. It also supports the development of new engineering, computational and informatics techniques for data collection, analyses and management.   The Encyclopedia of Computational Neuroscience will be consultable by both researchers and graduate level students. It will be a dynamic, living reference, continually updatable and containing linkouts and multimedia content whenever relevant.

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