Maximum a Posteriori Models for Cortical Modeling: Feature Detectors, Topography and Modularity - Couverture souple

Weber, Cornelius

 
9783836496988: Maximum a Posteriori Models for Cortical Modeling: Feature Detectors, Topography and Modularity
Couverture souple
ISBN 10 : 3836496984 ISBN 13 : 9783836496988
Editeur : VDM Verlag Dr. Müller, 2008

Synopsis

This thesis shows in a top-down modeling approach that unsupervised learning rules of neural networks can account for the development of cortical neural connections.Sections 1, Introduction, and 2, The Cortex, comprise biological foundations about the cortex: its areas and their mutual connectivity, cell layers and the mechanisms which govern the development of neural connections. These data supply the goal of modeling as well as the motivation for the methods which are used. Sections 3, Theory, and 4, Models, describe the theory and how to derive models from it. Section 5 presents Results, and section 6, a Discussion.In this work it is demonstrated that using a sparsely coded Boltzmann machine, neurons emerge which have localized and orientation selective receptive fields like those observed in primary visual cortex. Another highlight is the demonstration of a high adaptability of model structures to the environment. Either parallelly or hierarchically organized modules will arise as an appropriate adaptation to the organization of the training data set.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

This thesis shows in a top-down modeling approach that unsupervised learning rules of neural networks can account for the development of cortical neural connections.Sections 1, Introduction, and 2, The Cortex, comprise biological foundations about the cortex: its areas and their mutual connectivity, cell layers and the mechanisms which govern the development of neural connections. These data supply the goal of modeling as well as the motivation for the methods which are used. Sections 3, Theory, and 4, Models, describe the theory and how to derive models from it. Section 5 presents Results, and section 6, a Discussion.In this work it is demonstrated that using a sparsely coded Boltzmann machine, neurons emerge which have localized and orientation selective receptive fields like those observed in primary visual cortex. Another highlight is the demonstration of a high adaptability of model structures to the environment. Either parallelly or hierarchically organized modules will arise as an appropriate adaptation to the organization of the training data set.

Biographie de l'auteur

Dr. Weber graduated in physics, Bielefeld, Germany, and received his PhD in computer science at the TU Berlin in 2000. Then he was a postdoctoral fellow in Brain and Cognitive Sciences, Rochester, USA. From 2002 to 2005 he was a research scientist in Hybrid Intelligent Systems, Sunderland, UK. Then, Junior Fellow at the FIAS Institute, Germany.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag Dr. Müller
Date d'édition
2008
Langue
anglais
ISBN 10 
3836496984
ISBN 13 
9783836496988
Reliure
Broché
Nombre de pages
112
Coordonnées du fabricant
non disponible
Personne responsable
non disponible