Proc. *Computational Neuroscience Meeting,
CNS'98*, Santa Barbara, July 26-30, 1998, special issue of
Neurocomputing, 26/27(1):925-932 (1999-07-26) (bibtex)

## Learning invariance manifolds.

Laurenz Wiskott

*Abstract:* A new algorithm for learning invariance manifolds is introduced
that allows a neuron to learn a non-linear input-output function to extract
invariant or rather slowly varying features from a vectorial input
sequence. This is demonstrated by a simple model of learning complex cell
responses. The algorithm is generalized to a group of neurons, referred to
as a Gibson-clique, to learn slowly varying features that are uncorrelated.
Since the input-output functions are non-linear, this technique can be
applied iteratively. This is demonstrated by a hierarchical network of
Gibson-cliques learning translation invariance.

*Keywords:* feed-forward network, higher-order units, invariances, manifolds,
unsupervised learning

### Relevant Project:

July 27, 2000, Laurenz Wiskott, http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/wiskott/