This monograph provides an overview of recruitment learning ap-proaches from a computational perspective. Recruitment learning is a unique machine learningtechnique that: (1) explains the physical or functional acquisition of new neurons in sparsely connected networks as a biologically plausible neural network method; (2) facilitates the acquisition of new knowledge to build and extendknowledge bases and ontologies as an artificial intelligence technique; (3) allows learning by use of background knowledge and a limited number of observations, consistent with psychological theory." Provides an overview of recruitment learning approaches from a computational perspective State-of-the-Art book Written by leading experts in this field INDICE: 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.
- ISBN: 978-3-642-14027-3
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 250
- Fecha Publicación: 18/07/2010
- Nº Volúmenes: 1
- Idioma: Inglés