title: Learning sensory-motor cortical mappings without training
creator: Spratling, Michael
creator: Hayes, Gillian
subject: Neural Nets
subject: Robotics
subject: Neural Modelling
description: This paper shows how the relationship between two arrays of artificial neurons, representing different cortical regions, can be learned. The algorithm enables each neural network to self-organise into a topological map of the domain it represents at the same time as the relationship between these maps is found. Unlike previous methods learning is achieved without a separate training phase; the algorithm which learns the mapping is also that which performs the mapping.
date: 1998
type: Conference Paper
type: PeerReviewed
format: application/postscript
identifier: http://cogprints.org/1106/2/cort_map.ps
identifier:   Spratling, Michael and Hayes, Gillian  (1998) Learning sensory-motor cortical mappings without training.  [Conference Paper]     
relation: http://cogprints.org/1106/