Self-organizing Map Formation: Foundations of Neural Computation

Pirmais vāks
Klaus Obermayer, Terrence Joseph Sejnowski
MIT Press, 2001 - 440 lappuses

This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting.

Contributors
J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang

No grāmatas satura

 

Saturs

Analysis of Linskers Simulations of Hebbian Rules
3
Toward a Theory of the Striate Cortex
19
Bayesian SelfOrganization Driven by Prior Probability Distributions
39
Dynamics and Formation of SelfOrganizing Maps
55
A Unifying Objective Function for Topographic Mappings
69
A Unifying
83
How to Generate Ordered Maps by Maximizing the Mutual
129
Models of Orientation and Ocular Dominance Columns in
141
Hyperparameter Selection for SelfOrganizing Maps
277
The Generative Topographic Mapping
291
SelfOrganization as an Iterative Kernel Smoothing Process
311
A Stochastic SelfOrganizing Map for Proximity Data
327
SelfOrganized Formation of Various InvariantFeature Filters
345
Faithful Representation of Separable Distributions
369
Dynamic Cell Structure Learns Perfectly Topology Preserving Map
385
An Analysis of the Elastic Net Approach to the Traveling
407

Development of Oriented Ocular Dominance Bands as
185
A SelfOrganizing Model of Color Blob Formation
213
A Type of Duality between SelfOrganizing Maps and Minimal
235
A Bayesian Analysis of SelfOrganizing Maps
249
Index
423
235
426
Autortiesības

Bieži izmantoti vārdi un frāzes

algorithm analysis approximation ASSOM basis vectors Bayes Bayesian blob cells component constraint correlations corresponding cortical maps data points data space DCS-GCS density derived dimensionality distribution Durbin dynamics eigenvalues eigenvectors elastic encoding equation estimate Euclidean feature maps Figure filters gaussian Hebbian Hebbian learning hyperparameters input vector iteration kernel smoothing Kohonen learning rule linear Linsker Luttrell Malsburg map formation Markov chain matrix Miller minimal modules neighbor neighborhood function Neural Computation Neural Networks neurons node noise nonlinear normalization rules Obermayer objective function ocular dominance optimization orientation preference output layer parameters patterns Po(xo principal curves probabilistic problem receptive fields reference vectors region representation sample Schulten Sejnowski self-organizing map simulations spatial statistical striate cortex structure subspace synaptic tion topological space topology topology preserving traveling salesman problem units update values variables vector quantization visual cortex Voronoi weight Willshaw x₁

Atsauces uz šo grāmatu

Par autoru (2001)

Klaus Obermayer is Professor of Computer Science and head of the Neural Information Processing Group at the Technical University of Berlin. Terrence J. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at the University of California, San Diego. He was a member of the advisory committee for the Obama administration's BRAIN initiative and is President of the Neural Information Processing (NIPS) Foundation. He has published twelve books, including (with Patricia Churchland) The Computational Brain (25th Anniversary Edition, MIT Press).

Bibliogrāfiskā informācija

NosaukumsSelf-organizing Map Formation: Foundations of Neural Computation
A Bradford book
Grāmatas Computational neuroscience 93. sējums,9125. izdevums. sējums
RedaktoriKlaus Obermayer, Terrence Joseph Sejnowski
Izdevums:ilustrēts
IzdevējsMIT Press, 2001
ISBN0262650606, 9780262650601
Apjoms440 lappuses
  
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