Paper on self-organizing topographic networks for classification
Jim Williamson
jrw at cns.bu.edu
Wed Aug 16 10:54:19 EDT 2000
I would like to announce the availability of a new paper,
accepted for publication in Neural Computation.
Available at: http://cns-web.bu.edu/pub/jrw/www/pubs.html
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SELF-ORGANIZATION OF TOPOGRAPHIC MIXTURE NETWORKS
USING ATTENTIONAL FEEDBACK
James R. Williamson
Department of Cognitive and Neural Systems
Boston University, Boston, MA 02215
Neural Computation, in press.
ABSTRACT
This paper proposes a neural network model of supervised learning
which employs biologically-motivated constraints of using local,
on-line, constructive learning. The model possesses two novel
learning mechanisms. The first is a network for learning topographic
mixtures. The network's internal category nodes are the mixture
components, which learn to encode smooth distributions in the input
space by taking advantage of topography in the input feature maps.
The second mechanism is an attentional biasing feedback circuit.
When the network makes an incorrect output prediction, this feedback
circuit modulates the learning rates of the category nodes, by
amounts based on the sharpness of their tuning, in order to improve
the network's prediction accuracy. The network is evaluated on
several standard classification benchmarks and shown to perform well
in comparison to other classifiers.
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