Proc. *5th Joint Symposium on Neural Computation,
JSNC'98*, San Diego, May 16, publ. University of California, San Diego,
pp. 196-203 (1998-05-16) (bibtex, paper.pdf)

## Learning invariance manifolds.

Laurenz Wiskott

*Abstract:* A new algorithm for learning invariance manifolds is introduced
that allows a neuron to learn a non-linear transfer function to extract
invariant or rather slowly varying features from a vectorial input
sequence. This is generalized to a group of neurons, referred to as a
Gibson-clique, to learn slowly varying features that are uncorrelated.
Since the transfer functions are non-linear, this technique can be applied
iteratively. Four examples demonstrating the properties of the learning
algorithm include learning complex cell response with one Gibson-clique and
learning translation invariance in a hierarchical network of
Gibson-cliques.

### Relevant Project:

June 2, 2008, Laurenz Wiskott, http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/wiskott/