Neural Comp. NEW Faster Access
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kohonen, T.
Right arrow Articles by Lappalainen, H.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Kohonen, T.
Right arrow Articles by Lappalainen, H.

Neural Computation, Vol 9, 1321-1344, Copyright © 1997 by The MIT Press

LETTERS

Self-organized Formation of Various Invariant-feature Filters in the Adaptive-subspace SOM

Teuvo Kohonen, Samuel Kaski and Harri Lappalainen

The adaptive-subspace self-organizing map (ASSOM) is a modular neural network architecture, the modules of which learn to identify input patterns subject to some simple transformations. The learning process is unsupervised, competitive, and related to that of the traditional SOM (self-organizing map). Each neural module becomes adaptively specific to some restricted class of transformations, and modules close to each other in the network become tuned to similar features in an orderly fashion. If different transformations exist in the input signals, different subsets of ASSOM units become tuned to these transformation classes.


This article has been cited by other articles:


Home page
 Neural Comput.Home page
G. de A. Barreto, A. F. R. Araujo, and S. C. Kremer
A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case
Neural Comput., June 1, 2003; 15(6): 1255 - 1320.
[Abstract] [Full Text] [PDF]

Home page
 Neural Comput.Home page
J. Hurri and A. Hyvarinen
Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video
Neural Comput., March 1, 2003; 15(3): 663 - 691.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
T. Kohonen
Modeling of automatic capture and focusing of visual attention
PNAS, July 23, 2002; 99(15): 9813 - 9818.
[Abstract] [Full Text] [PDF]


HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1997 by The MIT Press.