title: Learning synaptic clusters for non-linear dendritic processing creator: Spratling, Michael creator: Hayes, Gillian subject: Neural Nets subject: Neural Modelling description: Nonlinear dendritic processing appears to be a feature of biological neurons and would also be of use in many applications of artificial neural networks. This paper presents a model of an initially standard linear unit which uses unsupervised learning to find clusters of inputs within which inactivity at one synapse can occlude the activity at the other synapses. date: 2000 type: Journal (Paginated) type: PeerReviewed format: application/postscript identifier: http://cogprints.org/1109/2/sigma_pi.ps identifier: Spratling, Michael and Hayes, Gillian (2000) Learning synaptic clusters for non-linear dendritic processing. [Journal (Paginated)] relation: http://cogprints.org/1109/