?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Learning+synaptic+clusters+for+non-linear+dendritic+processing&rft.creator=Spratling%2C+Michael&rft.creator=Hayes%2C+Gillian&rft.subject=Neural+Nets&rft.subject=Neural+Modelling&rft.description=Nonlinear+dendritic+processing+appears+to+be+a+feature+of+biological+neurons+and+would+also+be+of+use+in+many+applications+of+artificial+neural+networks.+This+paper+presents+a+model+of+an+initially+standard+linear+unit+which+uses+unsupervised+learning+to+find+clusters+of+inputs+within+which+inactivity+at+one+synapse+can+occlude+the+activity+at+the+other+synapses.&rft.date=2000&rft.type=Journal+(Paginated)&rft.type=PeerReviewed&rft.format=application%2Fpostscript&rft.identifier=http%3A%2F%2Fcogprints.org%2F1109%2F2%2Fsigma_pi.ps&rft.identifier=++Spratling%2C+Michael+and+Hayes%2C+Gillian++(2000)+Learning+synaptic+clusters+for+non-linear+dendritic+processing.++%5BJournal+(Paginated)%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F1109%2F