Categorization in IT and PFC: Model and Experiments
In 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 uni...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/7270 |
| _version_ | 1826206372590518272 |
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| author | Knoblich, Ulf Freedman, David J. Riesenhuber, Maximilian |
| author_facet | Knoblich, Ulf Freedman, David J. Riesenhuber, Maximilian |
| author_sort | Knoblich, Ulf |
| collection | MIT |
| description | In 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. |
| first_indexed | 2024-09-23T13:28:49Z |
| id | mit-1721.1/7270 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:28:49Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/72702019-04-12T08:34:35Z Categorization in IT and PFC: Model and Experiments Knoblich, Ulf Freedman, David J. Riesenhuber, Maximilian AI categorization IT PFC computational neuroscience model HMAX In 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. 2004-10-20T21:05:01Z 2004-10-20T21:05:01Z 2002-04-18 AIM-2002-007 CBCL-216 http://hdl.handle.net/1721.1/7270 en_US AIM-2002-007 CBCL-216 11 p. 1497623 bytes 678374 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI categorization IT PFC computational neuroscience model HMAX Knoblich, Ulf Freedman, David J. Riesenhuber, Maximilian Categorization in IT and PFC: Model and Experiments |
| title | Categorization in IT and PFC: Model and Experiments |
| title_full | Categorization in IT and PFC: Model and Experiments |
| title_fullStr | Categorization in IT and PFC: Model and Experiments |
| title_full_unstemmed | Categorization in IT and PFC: Model and Experiments |
| title_short | Categorization in IT and PFC: Model and Experiments |
| title_sort | categorization in it and pfc model and experiments |
| topic | AI categorization IT PFC computational neuroscience model HMAX |
| url | http://hdl.handle.net/1721.1/7270 |
| work_keys_str_mv | AT knoblichulf categorizationinitandpfcmodelandexperiments AT freedmandavidj categorizationinitandpfcmodelandexperiments AT riesenhubermaximilian categorizationinitandpfcmodelandexperiments |