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Visual cortex: one for all and all for one

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 Vanni_presentation_VIPP2010 Presentation Transcript

  • Visual cortex: one for all and all for one
    • Simo Vanni, MD PhD
    • Vision systems physiology group
    • Brain Research Unit, Low Temperature Laboratory
    • Aalto University
    • School of Science and Technology
  • What is common to subjective experience, visual perception, and neural activation? Statistics of individual visual environment
  • Sensory and motor areas in human brain Van Essen (2003) in Visual Neurosciences 27 % 7 % 7 % 8 %
  • Felleman & Van Essen, Cerebral Cortex 1 (1991) 1-47
  • Felleman & Van Essen, Cerebral Cortex 1 (1991) 1-47
  • Mapping of visual cortex Courtesy of Linda Henriksson
  • Visual information Correlated features Sparse coding Independent representations
  • Visual information Correlated features Sparse coding Independent representations
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  • 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.
  • From the eye to the brain Retina Thalamus Cerebral, cortex
  • Correlated phases at multiple scales Henriksson, Hyvärinen & Vanni. J Neurosci 29 (2009) 14342-14351
  • Sensitivity to correlated phase Henriksson, Hyvärinen & Vanni. J Neurosci 29 (2009) 14342-14351
  • Orientation correlations Geissler et al., Vision Research 41 (2001) 711–724
  • A neuron learns to be selective Dyan & Abbot: Theoretical Neuroscience (2001) MIT Press
  • Different tuning functions for orientation Dyan & Abbot: Theoretical Neuroscience (2001) MIT Press Neuron 1 Neuron 2 Neuron 3 Neuron 4
  • Multiple systems on top of each other Hübener ym, J Neurosci 17 (1997) 9270-9284 Ocular dominance and orientation Spatial frequency and orientation
  • What is a visual object…
  • http://members.lycos.nl/amazingart/E/20.html
  • Visual information is the regularities of co-occurence, ”statistics”, of our environment
  • Visual information Correlated features Sparse coding Independent representations
  • What is sparse coding
    • Many units are inactive, while few units are strongly active (population sparseness)
    • A single unit has on average low activity, with occasional bursts at high frequency (lifetime sparseness)
    • Mean energy consumption down
    • Computational benefits
  • Sparse coding Vinje & Gallant, Science 287 (2000) 1273-1276
  • 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
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  •  
  • Visual information Correlated features Sparse coding Independent representations
  • Context supports perception
  • Context distorts perception
  • Area tuning function Varying size of drifting gratings Courtesy of Lauri Nurminen and Markku Kilpeläinen
  • Receptive field Angelucci & Bressloff, Prog Brain Res 154 (2006) 93 – 120
  • A block model of surround interaction Schwabe et al. J Neurosci 26 (2006) 9117-9129 Afferent input Low-level area High-level area
  • Subtractive normalization model applied to non-linear interactions in the human cortex What visual information has to do with surround modulation?
  • Stimuli Vanni & Rosenström, in preparation
  • Centre response covaries with the surround response Vanni & Rosenström, in preparation VOIcentre
  • Active voxels for centre are suppressed during simultaneous presentation Vanni & Rosenström, in preparation VOIcentre
  • Suppression (red) is surrounded by facilitation (blue) Vanni & Rosenström, in preparation
  • 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.
  • Independence, decorrelation
    • Effective use of narrow dynamic range (surround modulation) and limited time (adaptation)
    • More explicit causal factors
    • Implemented by Hebbian and anti-Hebbian learning rules
    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.
  • A hypothesis of the visual brain
    • Our brain learns a hierarchical model of our visual environment
    • Each neuron in the model is sensitive to a set of correlated features in the environment
    • Population of neurons in this model form a sparse representation by relatively independent units
    • The tuning functions may be the most informative dimensions of visual environment
  • Collaborators
    • Aalto University
      • Linda Henriksson
      • Lauri Nurminen
      • Tom Rosenström
    • University of Helsinki
      • Jarmo Hurri
      • Aapo Hyvärinen
      • Markku Kilpeläinen
      • Pentti Laurinen
    • ANU, Canberra
      • Andrew James