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Autonomous development of decorrelation filters in neural networks with recurrent inhibition.

Authors
  • Jonker, H J
  • Coolen, A C
  • Denier van der Gon, J J
Type
Published Article
Journal
Network (Bristol, England)
Publication Date
Aug 01, 1998
Volume
9
Issue
3
Pages
345–362
Identifiers
PMID: 9861995
Source
Medline
License
Unknown

Abstract

We perform a quantitative analysis of information processing in a simple neural network model with recurrent inhibition. We postulate that both excitatory and inhibitory synapses continually adapt according to the following Hebbian-type rules: for excitatory synapses correlated pre- and post-synaptic activity induces enhanced excitation; for inhibitory synapses it induces enhanced inhibition. Following synaptic equilibration in unsupervised learning processes, the model is found to perform a novel type of principal-component analysis which involves filtering and decorrelation. In the light of these results we discuss the possible role of the granule-/Golgi-cell subnetwork in the cerebellum.

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