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dc.contributor.authorKnoblich, Ulfen_US
dc.contributor.authorFreedman, David J.en_US
dc.contributor.authorRiesenhuber, Maximilianen_US
dc.date.accessioned2004-10-20T21:05:01Z
dc.date.available2004-10-20T21:05:01Z
dc.date.issued2002-04-18en_US
dc.identifier.otherAIM-2002-007en_US
dc.identifier.otherCBCL-216en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7270
dc.description.abstractIn a recent experiment, Freedman et al. recorded from inferotemporal (IT) and prefrontal cortices (PFC) of monkeys performing a "cat/dog" categorization task (Freedman 2001 and Freedman, Riesenhuber, Poggio, Miller 2001). In this paper we analyze the tuning properties of view-tuned units in our HMAX model of object recognition in cortex (Riesenhuber 1999) using the same paradigm and stimuli as in the experiment. We then compare the simulation results to the monkey inferotemporal neuron population data. We find that view-tuned model IT units that were trained without any explicit category information can show category-related tuning as observed in the experiment. This suggests that the tuning properties of experimental IT neurons might primarily be shaped by bottom-up stimulus-space statistics, with little influence of top-down task-specific information. The population of experimental PFC neurons, on the other hand, shows tuning properties that cannot be explained just by stimulus tuning. These analyses are compatible with a model of object recognition in cortex (Riesenhuber 2000) in which a population of shape-tuned neurons provides a general basis for neurons tuned to different recognition tasks.en_US
dc.format.extent11 p.en_US
dc.format.extent1497623 bytes
dc.format.extent678374 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-2002-007en_US
dc.relation.ispartofseriesCBCL-216en_US
dc.subjectAIen_US
dc.subjectcategorization IT PFC computational neuroscience model HMAXen_US
dc.titleCategorization in IT and PFC: Model and Experimentsen_US


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