This lecture note volume is mainly about the recent development that connected neural network modeling to the theoretical physics of disordered systems. It gives a detailed account of the (Little-) Hopfield model and its ramifications concerning non-orthogonal and hierarchical patterns, short-term m...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Geszti, Tamás (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Singapore World Scientific Pub. Co. c1990
Schlagworte:	Neural circuitry / Models Neural networks (Computer science) Neural computers Neuronales Netz Modell Nervennetz
Online-Zugang:	FHN01 Volltext
Zusammenfassung:	This lecture note volume is mainly about the recent development that connected neural network modeling to the theoretical physics of disordered systems. It gives a detailed account of the (Little-) Hopfield model and its ramifications concerning non-orthogonal and hierarchical patterns, short-term memory, time sequences, and dynamical learning algorithms. It also offers a brief introduction to computation in layered feed-forward networks, trained by back-propagation and other methods. Kohonen's self-organizing feature map algorithm is discussed in detail as a physical ordering process. The book offers a minimum complexity guide through the often cumbersome theories developed around the Hopfield model. The physical model for the Kohonen self-organizing feature map algorithm is new, enabling the reader to better understand how and why this fascinating and somewhat mysterious tool works
Beschreibung:	viii, 143 p. ill
ISBN:	9789814434492