?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Learning+sensory-motor+cortical+mappings+without+training&rft.creator=Spratling%2C+Michael&rft.creator=Hayes%2C+Gillian&rft.subject=Neural+Nets&rft.subject=Robotics&rft.subject=Neural+Modelling&rft.description=This+paper+shows+how+the+relationship+between+two+arrays+of+artificial+neurons%2C+representing+different+cortical+regions%2C+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%3B+the+algorithm+which+learns+the+mapping+is+also+that+which+performs+the+mapping.&rft.date=1998&rft.type=Conference+Paper&rft.type=PeerReviewed&rft.format=application%2Fpostscript&rft.identifier=http%3A%2F%2Fcogprints.org%2F1106%2F2%2Fcort_map.ps&rft.identifier=++Spratling%2C+Michael+and+Hayes%2C+Gillian++(1998)+Learning+sensory-motor+cortical+mappings+without+training.++%5BConference+Paper%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F1106%2F