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"Generative Models for Discovering Sparse Distributed Representations"

	       Geoffrey E. Hinton and Zoubin Ghahramani
		    Department of Computer Science
			University of Toronto


			       ABSTRACT

We describe a hierarchical, generative model that can be viewed as a
non-linear generalization of factor analysis and can be implemented in
a neural network.  The model uses bottom-up, top-down and lateral
connections to perform Bayesian perceptual inference correctly.  Once
perceptual inference has been performed the connection strengths can
be updated using a very simple learning rule that only requires
locally available information. We demonstrate that the network learns
to extract sparse, distributed, hierarchical representations.


The paper is available at

http://www.cs.toronto.edu/~hinton/ftp/RGBN.ps.Z