Recruitment Learning [recurso electrónico] / by Joachim Diederich, Cengiz Günay, James M. Hogan.

Por: Diederich, Joachim [author.]Colaborador(es): Günay, Cengiz [author.] | Hogan, James M [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Studies in Computational Intelligence ; 303Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: X, 314 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642140280Tema(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 519 Clasificación LoC:TA329-348TA640-643Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer eBooksResumen: 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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Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos TA329 -348 (Browse shelf(Abre debajo)) 1 No para préstamo 374652-2001

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.

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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