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      Metamers of the ventral stream

      research-article
      1 , 1 , 2 , 3
      Nature neuroscience

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          Abstract

          The human capacity to recognize complex visual patterns emerges in a sequence of brain areas known as the ventral stream, beginning with primary visual cortex (V1). We develop a population model for mid-ventral processing, in which non-linear combinations of V1 responses are averaged within receptive fields that grow with eccentricity. To test the model, we generate novel forms of visual metamers — stimuli that differ physically, but look the same. We develop a behavioral protocol that uses metameric stimuli to estimate the receptive field sizes in which the model features are represented. Because receptive field sizes change along the ventral stream, the behavioral results can identify the visual area corresponding to the representation. Measurements in human observers implicate V2, providing a new functional account of this area. The model explains deficits of peripheral vision known as “crowding”, and provides a quantitative framework for assessing the capabilities of everyday vision.

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          Most cited references44

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          Robust object recognition with cortex-like mechanisms.

          We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex.
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            Interaction effects in parafoveal letter recognition.

            H. Bouma (1970)
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              Inferotemporal cortex and object vision.

              K Tanaka (1996)
              Cells in area TE of the inferotemporal cortex of the monkey brain selectively respond to various moderately complex object features, and those that cluster in a columnar region that runs perpendicular to the cortical surface respond to similar features. Although cells within a column respond to similar features, their selectivity is not necessarily identical. The data of optical imaging in TE have suggested that the borders between neighboring columns are not discrete; a continuous mapping of complex feature space within a larger region contains several partially overlapped columns. This continuous mapping may be used for various computations, such as production of the image of the object at different viewing angles, illumination conditions, and articulation poses.
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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nature neuroscience
                1097-6256
                1546-1726
                13 July 2011
                14 August 2011
                1 March 2012
                : 14
                : 9
                : 1195-1201
                Affiliations
                [1 ]Center for Neural Science, New York University, 6 Washington Place, New York, NY 10003, USA
                [2 ]Courant Institute of Mathematical Sciences, New York University, 6 Washington Place, New York, NY 10003, USA
                [3 ]Howard Hughes Medical Institute, New York University, 6 Washington Place, New York, NY 10003, USA
                Author notes
                Correspondence should be addressed to J.F. ( freeman@ 123456cns.nyu.edu )
                Article
                hhmipa309528
                10.1038/nn.2889
                3164938
                21841776
                63f1d392-0ef7-4db8-9b6b-176c4334912f

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: Howard Hughes Medical Institute :
                Award ID: || HHMI_
                Categories
                Article

                Neurosciences
                Neurosciences

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