Vanni_presentation_VIPP2010

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Visual cortex: one for all and all for one

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  • Helppo fenomenaalinen prosessi mutta vaikea computationaalinen prosessi Visuaalisen informaation käsittelyyn menee aikaa Tutkimalla aivojen rakenteita ja toimintaa voidaan yrittää ymmärtää tiedonkäsittelyn periaatteita Tiedonkäsittelyn ajallinen järjestys kertoo mitä aivokuoren yksiköitä tarvitaan missäkin tiedonkäsittelyn vaiheessa Perustietämys apinoista
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  • Vanni_presentation_VIPP2010

    1. 1. Visual cortex: one for all and all for one <ul><li>Simo Vanni, MD PhD </li></ul><ul><li>Vision systems physiology group </li></ul><ul><li>Brain Research Unit, Low Temperature Laboratory </li></ul><ul><li>Aalto University </li></ul><ul><li>School of Science and Technology </li></ul>
    2. 2. What is common to subjective experience, visual perception, and neural activation? Statistics of individual visual environment
    3. 3. Sensory and motor areas in human brain Van Essen (2003) in Visual Neurosciences 27 % 7 % 7 % 8 %
    4. 4. Felleman & Van Essen, Cerebral Cortex 1 (1991) 1-47
    5. 5. Felleman & Van Essen, Cerebral Cortex 1 (1991) 1-47
    6. 6. Mapping of visual cortex Courtesy of Linda Henriksson
    7. 7. Visual information Correlated features Sparse coding Independent representations
    8. 8. Visual information Correlated features Sparse coding Independent representations
    9. 10. Pixel intensity correlations Distance Distance Distance (pixels) Correlation From: Hyvärinen et al. (2009) Natural Image Statistics : A Probabilistic Approach to Early Computational Vision. London: Springer.
    10. 11. From the eye to the brain Retina Thalamus Cerebral, cortex
    11. 12. Correlated phases at multiple scales Henriksson, Hyvärinen & Vanni. J Neurosci 29 (2009) 14342-14351
    12. 13. Sensitivity to correlated phase Henriksson, Hyvärinen & Vanni. J Neurosci 29 (2009) 14342-14351
    13. 14. Orientation correlations Geissler et al., Vision Research 41 (2001) 711–724
    14. 15. A neuron learns to be selective Dyan & Abbot: Theoretical Neuroscience (2001) MIT Press
    15. 16. Different tuning functions for orientation Dyan & Abbot: Theoretical Neuroscience (2001) MIT Press Neuron 1 Neuron 2 Neuron 3 Neuron 4
    16. 17. Multiple systems on top of each other Hübener ym, J Neurosci 17 (1997) 9270-9284 Ocular dominance and orientation Spatial frequency and orientation
    17. 18. What is a visual object…
    18. 19. http://members.lycos.nl/amazingart/E/20.html
    19. 20. Visual information is the regularities of co-occurence, ”statistics”, of our environment
    20. 21. Visual information Correlated features Sparse coding Independent representations
    21. 22. What is sparse coding <ul><li>Many units are inactive, while few units are strongly active (population sparseness) </li></ul><ul><li>A single unit has on average low activity, with occasional bursts at high frequency (lifetime sparseness) </li></ul><ul><li>Mean energy consumption down </li></ul><ul><li>Computational benefits </li></ul>
    22. 23. Sparse coding Vinje & Gallant, Science 287 (2000) 1273-1276
    23. 24. Sparse coding of different tuning functions in the primary visual cortex Position Eye (stereo image) Spatial frequency (scale) Orientation Direction and speed of motion Wavelength (color) Courtesy of Aapo Hyvärinen
    24. 27. Visual information Correlated features Sparse coding Independent representations
    25. 28. Context supports perception
    26. 29. Context distorts perception
    27. 30. Area tuning function Varying size of drifting gratings Courtesy of Lauri Nurminen and Markku Kilpeläinen
    28. 31. Receptive field Angelucci & Bressloff, Prog Brain Res 154 (2006) 93 – 120
    29. 32. A block model of surround interaction Schwabe et al. J Neurosci 26 (2006) 9117-9129 Afferent input Low-level area High-level area
    30. 33. Subtractive normalization model applied to non-linear interactions in the human cortex What visual information has to do with surround modulation?
    31. 34. Stimuli Vanni & Rosenström, in preparation
    32. 35. Centre response covaries with the surround response Vanni & Rosenström, in preparation VOIcentre
    33. 36. Active voxels for centre are suppressed during simultaneous presentation Vanni & Rosenström, in preparation VOIcentre
    34. 37. Suppression (red) is surrounded by facilitation (blue) Vanni & Rosenström, in preparation
    35. 38. Efficient coding Response to stimulus A, A’ Response to stimulus B, B’ A’ = A – dB B’ = B – dA Barlow, H., and Földiák, P. (1989). In: The computing neuron. R. Durbin, et al., eds. (Boston, Addison-Wesley Longman Publishing Co., Inc), pp. 54-72.
    36. 39. Independence, decorrelation <ul><li>Effective use of narrow dynamic range (surround modulation) and limited time (adaptation) </li></ul><ul><li>More explicit causal factors </li></ul><ul><li>Implemented by Hebbian and anti-Hebbian learning rules </li></ul>Barlow, H., and Földiák, P. (1989). In: The computing neuron. R. Durbin, et al., eds. (Boston, Addison-Wesley Longman Publishing Co., Inc), pp. 54-72.
    37. 40. A hypothesis of the visual brain <ul><li>Our brain learns a hierarchical model of our visual environment </li></ul><ul><li>Each neuron in the model is sensitive to a set of correlated features in the environment </li></ul><ul><li>Population of neurons in this model form a sparse representation by relatively independent units </li></ul><ul><li>The tuning functions may be the most informative dimensions of visual environment </li></ul>
    38. 41. Collaborators <ul><li>Aalto University </li></ul><ul><ul><li>Linda Henriksson </li></ul></ul><ul><ul><li>Lauri Nurminen </li></ul></ul><ul><ul><li>Tom Rosenström </li></ul></ul><ul><li>University of Helsinki </li></ul><ul><ul><li>Jarmo Hurri </li></ul></ul><ul><ul><li>Aapo Hyvärinen </li></ul></ul><ul><ul><li>Markku Kilpeläinen </li></ul></ul><ul><ul><li>Pentti Laurinen </li></ul></ul><ul><li>ANU, Canberra </li></ul><ul><ul><li>Andrew James </li></ul></ul>

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