A multi-layer sparse coding network learns contour coding from natural images
An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spat...
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| Veröffentlicht in: | Vision research (Oxford) Jg. 42; H. 12; S. 1593 - 1605 |
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01.06.2002
Elsevier Science |
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| Abstract | An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spatial organization (topography) of the cells, can be understood as efficient coding of natural images. Here we extend the framework by considering how the responses of complex cells could be sparsely represented by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. In addition, contour integration could be interpreted as top-down inference in the presented model. |
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| AbstractList | An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spatial organization (topography) of the cells, can be understood as efficient coding of natural images. Here we extend the framework by considering how the responses of complex cells could be sparsely represented by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. In addition, contour integration could be interpreted as top-down inference in the presented model. An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spatial organization (topography) of the cells, can be understood as efficient coding of natural images. Here we extend the framework by considering how the responses of complex cells could be sparsely represented by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. In addition, contour integration could be interpreted as top-down inference in the presented model.An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spatial organization (topography) of the cells, can be understood as efficient coding of natural images. Here we extend the framework by considering how the responses of complex cells could be sparsely represented by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. In addition, contour integration could be interpreted as top-down inference in the presented model. |
| Author | Hyvärinen, Aapo Hoyer, Patrik O |
| Author_xml | – sequence: 1 givenname: Patrik O surname: Hoyer fullname: Hoyer, Patrik O email: patrik.hoyer@hut.fi – sequence: 2 givenname: Aapo surname: Hyvärinen fullname: Hyvärinen, Aapo |
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| Keywords | Neural networks Contours Cortex Independent component analysis Natural images Learning Visual cortex Coding Contour Vision Central nervous system Perception Simulation model Neural network Mathematical model Brain (vertebrata) |
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| SubjectTerms | Biological and medical sciences Cerebral Cortex - physiology Contours Cortex Fundamental and applied biological sciences. Psychology Humans Independent component analysis Models, Neurological Natural images Nerve Net Neural networks Neurophysiology Perception Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Vision Vision, Ocular - physiology |
| Title | A multi-layer sparse coding network learns contour coding from natural images |
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