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Sinopsis
One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, "Global Analysis of Recurrent Neural Net works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, "Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns" by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.
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Reseña del editor
One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, "Global Analysis of Recurrent Neural Net works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, "Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns" by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.
Reseña del editor
This collection of articles by leading researchers in neural networks responds to the urgent need for timely and comprehensive reviews in a multidisciplinary, rapidly developing field of research. It continues the themes of the previous volume, but shifts its focus to more practical matters, such as data storage and retrieval, and the recognition of handwriting.
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Models of Neural Networks III: Association, Generalization, and Representation (Physics of Neural Networks)
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Models of Neural Networks III: Association, Generalization, and Representation (Physics of Neural Networks)
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Models of Neural Networks III
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Springer New York Sep 2012, 2012
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ISBN 13: 9781461268826
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, 'Global Analysis of Recurrent Neural Net works,' by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, 'Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns' by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization. 328 pp. Englisch. Nº de ref. del artículo: 9781461268826
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Models of Neural Networks III
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Models of Neural Networks III: Association, Generalization, and Representation
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Models of Neural Networks III
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Models of Neural Networks III
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Models of Neural Networks III | Association, Generalization, and Representation
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Taschenbuch. Condición: Neu. Models of Neural Networks III | Association, Generalization, and Representation | Eytan Domany (u. a.) | Taschenbuch | xiii | Englisch | 2012 | Springer | EAN 9781461268826 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 106118775
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Models of Neural Networks III
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Springer New York, Springer New York Sep 2012, 2012
ISBN 10: 1461268826
ISBN 13: 9781461268826
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: Neu. Neuware -One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, 'Global Analysis of Recurrent Neural Net works,' by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, 'Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns' by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch. Nº de ref. del artículo: 9781461268826
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Models of Neural Networks III : Association, Generalization, and Representation
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Springer New York, Springer New York, 2012
ISBN 10: 1461268826
ISBN 13: 9781461268826
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, 'Global Analysis of Recurrent Neural Net works,' by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, 'Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns' by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization. Nº de ref. del artículo: 9781461268826
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