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Recruitment Learning: Studies in Computational Intelligence, cartea 303

Autor Joachim Diederich, Cengiz Gunay, James M. Hogan
en Limba Engleză Paperback – dec 2012
This book presents a fascinating and self-contained account of "recruitment learning", a model and theory of fast learning in the neocortex. In contrast to the more common attractor network paradigm for long- and short-term memory, recruitment learning focuses on one-shot learning or "chunking" of arbitrary feature conjunctions that co-occur in single presentations. The book starts with a comprehensive review of the historic background of recruitment learning, putting special emphasis on the ground-breaking work of D.O. Hebb, W.A.Wickelgren, J.A.Feldman, L.G.Valiant, and L. Shastri.Afterwards a thorough mathematical analysis of the model is presented which shows that recruitment is indeed a plausible mechanism of memory formation in the neocortex. A third part extends the main concepts towards state-of-the-art spiking neuron models and dynamic synchronization as a tentative solution of the binding problem. The book further discusses the possible role of adult neurogenesis for recruitment. These recent developments put the theory of recruitment learning at the forefront of research on biologically inspired memory models and make the book an important and timely contribution to the field.
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Specificații

ISBN-13: 9783642265471
ISBN-10: 3642265472
Pagini: 324
Ilustrații: X, 314 p. 109 illus., 33 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.45 kg
Ediția:2011
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

PART I: Recruitment in Discrete Time Neural Networks .- Recruitment Learning – An Introduction.- One-shot learning - Specialization and Generalization.- Connectivity and Candidate Structures.- Representation and Recruitment.- Cognitive Applications .- PART II: Recruitment in Continuous Time Neural Networks.- Spiking Neural Networks and Temporal Binding .- Synchronised Recruitment in Cortical .- The Stability of Recruited Concepts.- Conclusions.

Textul de pe ultima copertă

This book presents a fascinating and self-contained account of "recruitment learning", a model and theory of fast learning in the neocortex. In contrast to the more common attractor network paradigm for long- and short-term memory, recruitment learning focuses on one-shot learning or "chunking" of arbitrary feature conjunctions that co-occur in single presentations. The book starts with a comprehensive review of the historic background of recruitment learning, putting special emphasis on the ground-breaking work of D.O. Hebb, W.A.Wickelgren, J.A.Feldman, L.G.Valiant, and L. Shastri.Afterwards a thorough mathematical analysis of the model is presented which shows that recruitment is indeed a plausible mechanism of memory formation in the neocortex. A third part extends the main concepts towards state-of-the-art spiking neuron models and dynamic synchronization as a tentative solution of the binding problem. The book further discusses the possible role of adult neurogenesis for recruitment. These recent developments put the theory of recruitment learning at the forefront of research on biologically inspired memory models and make the book an important and timely contribution to the field.

Caracteristici

Provides an overview of recruitment learning approaches from a computational perspective State-of-the-Art book Written by leading experts in this field