Computational role of eccentricity dependent cortical magnification
We develop a sampling extension of M-theory focused on invariance to scale and translation. Quite surprisingly, the theory predicts an architecture of early vision with increasing receptive field sizes and a high resolution fovea — in agreement with data about the cortical magnification factor, V1 a...
| Main Authors: | , , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
Center for Brains, Minds and Machines (CBMM), arXiv
2015
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/100181 |
| _version_ | 1811079397712592896 |
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| author | Poggio, Tomaso Mutch, Jim Isik, Leyla |
| author_facet | Poggio, Tomaso Mutch, Jim Isik, Leyla |
| author_sort | Poggio, Tomaso |
| collection | MIT |
| description | We develop a sampling extension of M-theory focused on invariance to scale and translation. Quite surprisingly, the theory predicts an architecture of early vision with increasing receptive field sizes and a high resolution fovea — in agreement with data about the cortical magnification factor, V1 and the retina. From the slope of the inverse of the magnification factor, M-theory predicts a cortical “fovea” in V1 in the order of 40 by 40 basic units at each receptive field size — corresponding to a foveola of size around 26 minutes of arc at the highest resolution, ≈6 degrees at the lowest resolution. It also predicts uniform scale invariance over a fixed range of scales independently of eccentricity, while translation invariance should depend linearly on spatial frequency. Bouma’s law of crowding follows in the theory as an effect of cortical area-by-cortical area pooling; the Bouma constant is the value expected if the signature responsible for recognition in the crowding experiments originates in V2. From a broader perspective, the emerging picture suggests that visual recognition under natural conditions takes place by composing information from a set of fixations, with each fixation providing recognition from a space-scale image fragment — that is an image patch represented at a set of increasing sizes and decreasing resolutions. |
| first_indexed | 2024-09-23T11:14:23Z |
| format | Technical Report |
| id | mit-1721.1/100181 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:14:23Z |
| publishDate | 2015 |
| publisher | Center for Brains, Minds and Machines (CBMM), arXiv |
| record_format | dspace |
| spelling | mit-1721.1/1001812019-04-12T12:18:06Z Computational role of eccentricity dependent cortical magnification Poggio, Tomaso Mutch, Jim Isik, Leyla Invariance Theories for Intelligence Machine Learning Vision We develop a sampling extension of M-theory focused on invariance to scale and translation. Quite surprisingly, the theory predicts an architecture of early vision with increasing receptive field sizes and a high resolution fovea — in agreement with data about the cortical magnification factor, V1 and the retina. From the slope of the inverse of the magnification factor, M-theory predicts a cortical “fovea” in V1 in the order of 40 by 40 basic units at each receptive field size — corresponding to a foveola of size around 26 minutes of arc at the highest resolution, ≈6 degrees at the lowest resolution. It also predicts uniform scale invariance over a fixed range of scales independently of eccentricity, while translation invariance should depend linearly on spatial frequency. Bouma’s law of crowding follows in the theory as an effect of cortical area-by-cortical area pooling; the Bouma constant is the value expected if the signature responsible for recognition in the crowding experiments originates in V2. From a broader perspective, the emerging picture suggests that visual recognition under natural conditions takes place by composing information from a set of fixations, with each fixation providing recognition from a space-scale image fragment — that is an image patch represented at a set of increasing sizes and decreasing resolutions. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216. 2015-12-10T23:15:14Z 2015-12-10T23:15:14Z 2014-06-06 Technical Report Working Paper Other http://hdl.handle.net/1721.1/100181 arXiv:1406.1770v1 en_US CBMM Memo Series;017 Attribution-NonCommercial 3.0 United States http://creativecommons.org/licenses/by-nc/3.0/us/ application/pdf Center for Brains, Minds and Machines (CBMM), arXiv |
| spellingShingle | Invariance Theories for Intelligence Machine Learning Vision Poggio, Tomaso Mutch, Jim Isik, Leyla Computational role of eccentricity dependent cortical magnification |
| title | Computational role of eccentricity dependent cortical magnification |
| title_full | Computational role of eccentricity dependent cortical magnification |
| title_fullStr | Computational role of eccentricity dependent cortical magnification |
| title_full_unstemmed | Computational role of eccentricity dependent cortical magnification |
| title_short | Computational role of eccentricity dependent cortical magnification |
| title_sort | computational role of eccentricity dependent cortical magnification |
| topic | Invariance Theories for Intelligence Machine Learning Vision |
| url | http://hdl.handle.net/1721.1/100181 |
| work_keys_str_mv | AT poggiotomaso computationalroleofeccentricitydependentcorticalmagnification AT mutchjim computationalroleofeccentricitydependentcorticalmagnification AT isikleyla computationalroleofeccentricitydependentcorticalmagnification |