?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Learning+in+the+Cerebellum+with+Sparse+Conjunctions+and+Linear+Separator+Algorithms&rft.creator=Harris%2C+Harlan&rft.creator=Reichler%2C+Jesse&rft.subject=Computational+Neuroscience&rft.subject=Neural+Modelling&rft.description=This+paper+investigates+potential+learning+rules+%0Ain+the+cerebellum.+We+review+evidence+that+input+to+the+cerebellum+is+%0Asparsely+expanded+by+granule+cells+into+a+very+wide+basis+vector%2C+%0Aand+that+Purkinje%0Acells+learn+to+compute+a+linear+separation+using+that+basis.%0AWe+review+learning+rules+employed+by+existing+cerebellar+models%2C+and+show%0Athat+recent+results+from+Computational+Learning+Theory+suggest+that%0Athe+standard+delta+rule+would+not+be+efficient.%0AWe+suggest+that+alternative%2C+attribute-efficient+learning+rules%2C+such+as+%0AWinnow+or+Incremental+Delta-Bar-Delta%2C+are+more+appropriate+for+cerebellar%0Amodeling%2C+and+support+this+position+with+results+from+a+computational+model.%0A&rft.publisher=IEEE&rft.contributor=Marko%2C+Kenneth&rft.contributor=Werbos%2C+Paul&rft.date=2001&rft.type=Conference+Paper&rft.type=PeerReviewed&rft.format=application%2Fpostscript&rft.identifier=http%3A%2F%2Fcogprints.org%2F2310%2F2%2Fsparsewinnow.ps&rft.identifier=++Harris%2C+Harlan+and+Reichler%2C+Jesse++(2001)+Learning+in+the+Cerebellum+with+Sparse+Conjunctions+and+Linear+Separator+Algorithms.++%5BConference+Paper%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F2310%2F