title: A model of cardinality blindness in inferotemporal cortex.
abstract: A classifier is cardinality invariant if it can classify more than one token of a single type at a time. We present a convolutional neural network (CNN) model of inferotemporal cortex (IT) and show that it is cardinality invariant. While the CNN is designed with translation invariance in mind, cardinality invariance is an emergent property. We speculate that translation invariance may lead to cardinality invariance in general, and particularly in IT. Recent investigations have shown that cells in IT are indeed cardinality blind. We also explore the implications of a cardinality blind classifier for vision overall, concentrating on visual attention and search.
authors.affiliation: Department of Computer Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand. hwalles@cs.otago.ac.nz
authors.forename: Hayden
authors.initials: H
authors.lastname: Walles
authors.forename: Alistair
authors.initials: A
authors.lastname: Knott
authors.forename: Anthony
authors.initials: A
authors.lastname: Robins
mesh_terms: D000818:Animals; D001288:Attention / Q000502:physiology; D001766:Blindness; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D013702:Temporal Lobe / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*