Models of Neural Networks III: Association, Generalization, and Representation (Physics of Neural Networks) : Association, Generalization, and Representation

Eytan Domany

ISBN 10: 0387943684 / ISBN 13: 9780387943688

Edité par SPRINGER NATURE, 1995

A propos de cet article

Gebraucht - Sehr gut SG -leichte Beschädigungen oder Verschmutzungen, ungelesenes Mängelexemplar, gestempelt - 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.

N° de réf. du vendeur INF1000503840

Détails bibliographiques

• Titre: Models of Neural Networks III: Association, Generalization, and Representation (Physics of Neural Networks) : Association, Generalization, and Representation
• Éditeur: SPRINGER NATURE
• Date de parution: 1995
• Langue: anglais
• ISBN à 10 chiffres: 0387943684
• ISBN à 13 chiffres: 9780387943688
• Reliure: Gebundene Ausgabe
• État de l'article: Sehr gut
• Dimensions: 241 L x 161 H x 25 P (en millimètres) 241 millimètres de largeur par 161 millimètres de hauteur par 25 millimètres de profondeur
• Poids: 633 grammes

A propos de ce titre

Synopsis

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.