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Emergence of complex cell properties by learning to generalize in natural scenes

Author

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  • Yan Karklin

    (Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Present address: Center for Neural Science, New York University, New York, New York, USA (Y.K.); Electrical Engineering and Computer Science Department, Case Western University, Cleveland, Ohio, USA and Wissenschaftskolleg (Institute for Advanced Study) zu Berlin, Germany (M.S.L.).)

  • Michael S. Lewicki

    (Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Present address: Center for Neural Science, New York University, New York, New York, USA (Y.K.); Electrical Engineering and Computer Science Department, Case Western University, Cleveland, Ohio, USA and Wissenschaftskolleg (Institute for Advanced Study) zu Berlin, Germany (M.S.L.).)

Abstract

Building the picture Complex visual scenes are made up of many component features such as edges and textures. Neurons in the early stages of the visual system are sensitive to individual features; it's implicitly believed that the nervous system must put them back together to signal conjunctions of different features, but how this is achieved is unknown. Yan Karklin and Michael Lewicki have developed a computational model of visual processing in which neural activity encodes statistical variations of features in images, establishing which ones are most likely to be associated with each other. Aspects of the model echo the nonlinear properties of some visual neurons, hinting at a possible functional interpretation for these properties.

Suggested Citation

  • Yan Karklin & Michael S. Lewicki, 2009. "Emergence of complex cell properties by learning to generalize in natural scenes," Nature, Nature, vol. 457(7225), pages 83-86, January.
    • Handle: RePEc:nat:nature:v:457:y:2009:i:7225:d:10.1038_nature07481
    DOI: 10.1038/nature07481
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    Citations

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    Cited by:

    1. Ruben Coen-Cagli & Peter Dayan & Odelia Schwartz, 2012. "Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-18, March.
    2. Jörn-Philipp Lies & Ralf M Häfner & Matthias Bethge, 2014. "Slowness and Sparseness Have Diverging Effects on Complex Cell Learning," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-11, March.
    3. Boris Vladimirskiy & Robert Urbanczik & Walter Senn, 2015. "Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-19, December.
    4. Iris I A Groen & Sennay Ghebreab & Victor A F Lamme & H Steven Scholte, 2012. "Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-16, October.
    5. Laurence Aitchison & Máté Lengyel, 2016. "The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-24, December.
    6. Jeffrey D Fitzgerald & Ryan J Rowekamp & Lawrence C Sincich & Tatyana O Sharpee, 2011. "Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-9, October.
    7. Qianli Yang & Edgar Walker & R. James Cotton & Andreas S. Tolias & Xaq Pitkow, 2021. "Revealing nonlinear neural decoding by analyzing choices," Nature Communications, Nature, vol. 12(1), pages 1-13, December.

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