Models of Neural Networks III: Association, Generalization, and Representation
Saved in:
| Other Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
1996
|
| Series: | Physics of Neural Networks
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| Subjects: | |
| Online Access: | Volltext |
| Item Description: | 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 Networks," 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 argument since has been shown to be rather susceptible to generalization |
| Physical Description: | 1 Online-Ressource (XIII, 311 p) |
| ISBN: | 9781461207238 9781461268826 |
| ISSN: | 0939-3145 |
| DOI: | 10.1007/978-1-4612-0723-8 |
Staff View
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:20:47Z |
| institution | BVB |
| isbn | 9781461207238 9781461268826 |
| issn | 0939-3145 |
| language | English |
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| physical | 1 Online-Ressource (XIII, 311 p) |
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| series2 | Physics of Neural Networks |
| spelling | Models of Neural Networks III Association, Generalization, and Representation edited by Eytan Domany, J. Leo Hemmen, Klaus Schulten New York, NY Springer New York 1996 1 Online-Ressource (XIII, 311 p) txt rdacontent c rdamedia cr rdacarrier Physics of Neural Networks 0939-3145 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 Networks," 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 argument since has been shown to be rather susceptible to generalization Physics Statistical Physics, Dynamical Systems and Complexity Domany, Eytan edt Hemmen, J. Leo edt Schulten, Klaus edt https://doi.org/10.1007/978-1-4612-0723-8 Verlag Volltext |
| spellingShingle | Models of Neural Networks III Association, Generalization, and Representation Physics Statistical Physics, Dynamical Systems and Complexity |
| title | Models of Neural Networks III Association, Generalization, and Representation |
| title_auth | Models of Neural Networks III Association, Generalization, and Representation |
| title_exact_search | Models of Neural Networks III Association, Generalization, and Representation |
| title_full | Models of Neural Networks III Association, Generalization, and Representation edited by Eytan Domany, J. Leo Hemmen, Klaus Schulten |
| title_fullStr | Models of Neural Networks III Association, Generalization, and Representation edited by Eytan Domany, J. Leo Hemmen, Klaus Schulten |
| title_full_unstemmed | Models of Neural Networks III Association, Generalization, and Representation edited by Eytan Domany, J. Leo Hemmen, Klaus Schulten |
| title_short | Models of Neural Networks III |
| title_sort | models of neural networks iii association generalization and representation |
| title_sub | Association, Generalization, and Representation |
| topic | Physics Statistical Physics, Dynamical Systems and Complexity |
| topic_facet | Physics Statistical Physics, Dynamical Systems and Complexity |
| url | https://doi.org/10.1007/978-1-4612-0723-8 |
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