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dc.contributor.authorPoggio, Tomaso
dc.contributor.authorMutch, Jim
dc.contributor.authorIsik, Leyla
dc.date.accessioned2015-12-10T23:15:14Z
dc.date.available2015-12-10T23:15:14Z
dc.date.issued2014-06-06
dc.identifier.urihttp://hdl.handle.net/1721.1/100181
dc.description.abstractWe 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.en_US
dc.description.sponsorshipThis work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216.en_US
dc.language.isoen_USen_US
dc.publisherCenter for Brains, Minds and Machines (CBMM), arXiven_US
dc.relation.ispartofseriesCBMM Memo Series;017
dc.rightsAttribution-NonCommercial 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/*
dc.subjectInvarianceen_US
dc.subjectTheories for Intelligenceen_US
dc.subjectMachine Learningen_US
dc.subjectVisionen_US
dc.titleComputational role of eccentricity dependent cortical magnificationen_US
dc.typeTechnical Reporten_US
dc.typeWorking Paperen_US
dc.typeOtheren_US
dc.identifier.citationarXiv:1406.1770v1en_US


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