Neural Comp. Sign up for ETOCS
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 Similar articles in PubMed
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 Becker, S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Becker, S.

Neural Computation, Vol 11, Issue 2 347-374, Copyright © 1999 by The MIT Press

ARTICLES

Implicit learning in 3D object recognition: the importance of temporal context

S Becker
Department of Psychology, Psychology Building, Room 312, Mc Master University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada. becker@mcmaster.ca.

A novel architecture and set of learning rules for cortical self-organization is proposed. The model is based on the idea that multiple information channels can modulate one another's plasticity. Features learned from bottom-up information sources can thus be influenced by those learned from contextual pathways, and vice versa. A maximum likelihood cost function allows this scheme to be implemented in a biologically feasible, hierarchical neural circuit. In simulations of the model, we first demonstrate the utility of temporal context in modulating plasticity. The model learns a representation that categorizes people's faces according to identity, independent of viewpoint, by taking advantage of the temporal continuity in image sequences. In a second set of simulations, we add plasticity to the contextual stream and explore variations in the architecture. In this case, the model learns a two-tiered representation, starting with a coarse view-based clustering and proceeding to a finer clustering of more specific stimulus features. This model provides a tenable account of how people may perform 3D object recognition in a hierarchical, bottom-up fashion.


This article has been cited by other articles:


Home page
 Neural Comput.Home page
O. Schwartz, T. J. Sejnowski, and P. Dayan
Soft mixer assignment in a hierarchical generative model of natural scene statistics.
Neural Comput., November 1, 2006; 18(11): 2680 - 2718.
[Abstract] [Full Text] [PDF]

Home page
 Neural Comput.Home page
M. Li and J. J. Clark
A Temporal Stability Approach to Position and Attention-Shift-Invariant Recognition
Neural Comput., November 1, 2004; 16(11): 2293 - 2321.
[Abstract] [Full Text] [PDF]

Home page
 J. Neurophysiol.Home page
K. P. Kording, C. Kayser, W. Einhauser, and P. Konig
How Are Complex Cell Properties Adapted to the Statistics of Natural Stimuli?
J Neurophysiol, January 1, 2004; 91(1): 206 - 212.
[Abstract] [Full Text]

Home page
 Neural Comput.Home page
J. C. Wiemer
The Time-Organized Map Algorithm: Extending the Self-Organizing Map to Spatiotemporal Signals
Neural Comput., May 1, 2003; 15(5): 1143 - 1171.
[Abstract] [Full Text] [PDF]


HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1999 by The MIT Press.