?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Unsupervised+Learning+of+Place+Cells%2C+Head-Direction+Cells%2C+and+Spatial-View+Cells+with+Slow+Feature+Analysis+on+Quasi-Natural+Videos&rft.creator=Franzius%2C+Mathias&rft.creator=Sprekeler%2C+Henning&rft.creator=Wiskott%2C+Prof.+Dr.+Laurenz&rft.subject=Computational+Neuroscience&rft.subject=Machine+Vision&rft.subject=Theoretical+Biology&rft.description=We+present+a+model+for+the+self-organized+formation+of+place+cells%2C+head-direction+cells%2C+and+spatial+view+cells+in+the+hippocampal+formation+based+on+unsupervised+learning+on+quasi-natural+visual+stimuli.+The+model+comprises+a+hierarchy+of+Slow+Feature+Analysis+(SFA)+nodes%2C+which+were+recently+shown+to+be+a+good+model+for+complex+cells+in+the+early+visual+system+(Berkes+and+Wiskott%2C+2005).+The+system+extracts+a+distributed+grid-like+representation+of+position+and+orientation%2C+which+is+transcoded+into+a+localized+place+field%2C+head+direction%2C+or+view+representation%2C+respectively%2C+by+sparse+coding.+The+type+of+cells+that+develops+depends+solely+on+the+relevant+input+statistics%2C+i.e+the+movement+pattern+of+the+simulated+animal.+The+numerical+simulations+are+complemented+by+a+mathematical+analysis+that+allows+us+to+accurately+predict+the+output+of+the+top+SFA+layer.+%0A%0A&rft.date=2007-04&rft.type=Preprint&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F5492%2F1%2FplaceFieldsManuscript_submitCogPrints.pdf&rft.identifier=++Franzius%2C+Mathias+and+Sprekeler%2C+Henning+and+Wiskott%2C+Prof.+Dr.+Laurenz++(2007)+Unsupervised+Learning+of+Place+Cells%2C+Head-Direction+Cells%2C+and+Spatial-View+Cells+with+Slow+Feature+Analysis+on+Quasi-Natural+Videos.++%5BPreprint%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F5492%2F