title: Autonomous development of decorrelation filters in neural networks with recurrent inhibition.
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.
authors.affiliation: Helmholtz Institute, University of Utrecht, The Netherlands.
authors.forename: H J
authors.initials: HJ
authors.lastname: Jonker
authors.forename: A C
authors.initials: AC
authors.lastname: Coolen
authors.forename: J J
authors.initials: JJ
authors.lastname: Denier van der Gon
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D003198:Computer Simulation; D007257:Information Theory; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology*; D016571:Neural Networks, Computer; D013569:Synapses / Q000502:physiology