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Visual cortex: one for all and all for one ,[object Object],[object Object],[object Object],[object Object],[object Object]
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
 
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 ,[object Object],[object Object],[object Object],[object Object]
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
 
 
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 ,[object Object],[object Object],[object Object],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 ,[object Object],[object Object],[object Object],[object Object]
Collaborators ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Vanni_presentation_VIPP2010

Notas do Editor

  1. 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
  2. 46
  3. 63
  4. 64
  5. 10
  6. 55
  7. 60
  8. 6
  9. 89
  10. 57