title: Hierarchical models of object recognition in cortex.
abstract: Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.
authors.affiliation: Department of Brain and Cognitive Sciences, Center for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.
authors.forename: M
authors.initials: M
authors.lastname: Riesenhuber
authors.forename: T
authors.initials: T
authors.lastname: Poggio
mesh_terms: D000818:Animals; D003198:Computer Simulation; D005556:Form Perception / Q000502:physiology*; D008251:Macaca; D011939:Mental Recall / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology*; D014794:Visual Fields / Q000502:physiology