Mapping of Visual Receptive Fields by Tomographic Reconstruction
The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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MIT Press
2013
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| Online Access: | http://hdl.handle.net/1721.1/78863 https://orcid.org/0000-0003-2668-7819 |
| _version_ | 1811080649425027072 |
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| author | Pipa, Gordon Chen, Zhe Neuenschwander, Sergio Lima, Bruss Brown, Emery N. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Pipa, Gordon Chen, Zhe Neuenschwander, Sergio Lima, Bruss Brown, Emery N. |
| author_sort | Pipa, Gordon |
| collection | MIT |
| description | The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data and the circular geometry of the stimulus presentation. We present a novel analysis approach to mapping V1 receptive fields that combines point-process generalized linear models (PPGLM) with tomographic reconstruction computed by filtered-back projection. We use the method to map the RF sizes and orientations of 251 V1 neurons recorded from two macaque monkeys during a moving bar experiment. Our cross-validated goodness-of-fit analyses show that the PPGLM provides a more accurate characterization of spike train data than analyses based on rate functions computed by the methods of spike-triggered averages or first-order Wiener-Volterra kernel. Our analysis leads to a new definition of RF size as the spatial area over which the spiking activity is significantly greater than baseline activity. Our approach yields larger RF sizes and sharper orientation tuning estimates. The tomographic reconstruction paradigm further suggests an efficient approach to choosing the number of directions and the number of trials per direction in designing moving bar experiments. Our results demonstrate that standard tomographic principles for image reconstruction can be adapted to characterize V1 RFs and that two fundamental properties, size and orientation, may be substantially different from what is currently reported. |
| first_indexed | 2024-09-23T11:34:38Z |
| format | Article |
| id | mit-1721.1/78863 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:34:38Z |
| publishDate | 2013 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/788632022-10-01T04:32:14Z Mapping of Visual Receptive Fields by Tomographic Reconstruction Pipa, Gordon Chen, Zhe Neuenschwander, Sergio Lima, Bruss Brown, Emery N. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery N. The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data and the circular geometry of the stimulus presentation. We present a novel analysis approach to mapping V1 receptive fields that combines point-process generalized linear models (PPGLM) with tomographic reconstruction computed by filtered-back projection. We use the method to map the RF sizes and orientations of 251 V1 neurons recorded from two macaque monkeys during a moving bar experiment. Our cross-validated goodness-of-fit analyses show that the PPGLM provides a more accurate characterization of spike train data than analyses based on rate functions computed by the methods of spike-triggered averages or first-order Wiener-Volterra kernel. Our analysis leads to a new definition of RF size as the spatial area over which the spiking activity is significantly greater than baseline activity. Our approach yields larger RF sizes and sharper orientation tuning estimates. The tomographic reconstruction paradigm further suggests an efficient approach to choosing the number of directions and the number of trials per direction in designing moving bar experiments. Our results demonstrate that standard tomographic principles for image reconstruction can be adapted to characterize V1 RFs and that two fundamental properties, size and orientation, may be substantially different from what is currently reported. National Institutes of Health (U.S.) (Grant DP1-OD003646) 2013-05-13T19:03:19Z 2013-05-13T19:03:19Z 2012-06 2011-10 Article http://purl.org/eprint/type/JournalArticle 0899-7667 1530-888X http://hdl.handle.net/1721.1/78863 Pipa, Gordon, Zhe Chen, Sergio Neuenschwander, Bruss Lima, and Emery N. Brown 2012Mapping of Visual Receptive Fields by Tomographic Reconstruction. Neural Computation 24(10): 2543–2578. https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.1162/NECO_a_00334 Neural Computation Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf MIT Press SFN |
| spellingShingle | Pipa, Gordon Chen, Zhe Neuenschwander, Sergio Lima, Bruss Brown, Emery N. Mapping of Visual Receptive Fields by Tomographic Reconstruction |
| title | Mapping of Visual Receptive Fields by Tomographic Reconstruction |
| title_full | Mapping of Visual Receptive Fields by Tomographic Reconstruction |
| title_fullStr | Mapping of Visual Receptive Fields by Tomographic Reconstruction |
| title_full_unstemmed | Mapping of Visual Receptive Fields by Tomographic Reconstruction |
| title_short | Mapping of Visual Receptive Fields by Tomographic Reconstruction |
| title_sort | mapping of visual receptive fields by tomographic reconstruction |
| url | http://hdl.handle.net/1721.1/78863 https://orcid.org/0000-0003-2668-7819 |
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