--- abstract: '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.' altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: 1998 conference: 6th European Symposium on Artificial Neural Networks (ESANN98) confloc: Brugges contact_email: ~ creators_id: [] creators_name: - family: Spratling given: Michael honourific: '' lineage: '' - family: Hayes given: Gillian honourific: '' lineage: '' date: 1998 date_type: published datestamp: 2000-11-15 department: ~ dir: disk0/00/00/11/06 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 1106 fileinfo: /style/images/fileicons/application_postscript.png;/1106/2/cort_map.ps full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'sensorimotor control, neural networks' lastmod: 2011-03-11 08:54:26 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 339-344 pubdom: FALSE publication: ~ publisher: ~ refereed: TRUE referencetext: ~ relation_type: [] relation_uri: [] reportno: ~ rev_number: 10 series: ~ source: ~ status_changed: 2007-09-12 16:36:24 subjects: - comp-sci-neural-nets - comp-sci-robot - neuro-mod succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Learning sensory-motor cortical mappings without training type: confpaper userid: 1040 volume: ~