vanni vipp2010 presentation_bu
TRANSCRIPT
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Visual cortex: one for all and all for one
Simo Vanni, MD PhDVision systems physiology group
Brain Research Unit, Low Temperature LaboratoryAalto University
School of Science and Technology
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What is common to subjective experience, visual perception, and neural
activation?
Statistics of individual visual environment
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Sensory and motor areas in human brain
Van Essen (2003) in Visual Neurosciences
27 %
7 % 7 %
8 %
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Felleman & Van Essen, Cerebral Cortex 1 (1991) 1-47
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Felleman & Van Essen, Cerebral Cortex 1 (1991) 1-47
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Mapping of visual cortex
Courtesy of Linda Henriksson
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Visual information
Correlated featuresSparse coding
Independent representations
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Visual information
Correlated featuresSparse coding
Independent representations
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Pixel intensity correlations
Dis
tanc
eDistance
Distance (pixels)
Cor
rela
tion
From: Hyvärinen et al. (2009) Natural Image Statistics : A Probabilistic Approach to Early Computational Vision. London: Springer.
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From the eye to the brain Retina
Thalamus
Cerebral, cortex
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Correlated phases at multiple scales
Henriksson, Hyvärinen & Vanni. J Neurosci 29 (2009) 14342-14351
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Sensitivity to correlated phase
Henriksson, Hyvärinen & Vanni. J Neurosci 29 (2009) 14342-14351
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Orientation correlations
Geissler et al., Vision Research 41 (2001) 711–724
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A neuron learns to be selective
Dyan & Abbot: Theoretical Neuroscience (2001) MIT Press
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Different tuning functions for orientation
Dyan & Abbot: Theoretical Neuroscience (2001) MIT Press
Neuron 1 Neuron 2 Neuron 3 Neuron 4
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Multiple systems on top of each other
Hübener ym, J Neurosci 17 (1997) 9270-9284
Ocular dominance and orientation Spatial frequency and orientation
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What is a visual object…
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http://members.lycos.nl/amazingart/E/20.html
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Visual information is the regularities of co-occurence, ”statistics”, of our
environment
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Visual information
Correlated featuresSparse coding
Independent representations
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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
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Sparse coding
Vinje & Gallant, Science 287 (2000) 1273-1276
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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 featuresSparse coding
Independent representations
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Context supports perception
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Context distorts perception
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Area tuning function
Varying size of drifting gratings
Courtesy of Lauri Nurminen and Markku Kilpeläinen
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Angelucci & Bressloff, Prog Brain Res 154 (2006) 93 – 120
Receptive field
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A block model of surround interaction
Schwabe et al. J Neurosci 26 (2006) 9117-9129
Afferent input
Low-level area
High-level area
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Subtractive normalization model applied to non-linear interactions in the human
cortex
What visual information has to do with surround modulation?
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Stimuli
Vanni & Rosenström, in preparation
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Centre response covaries with the surround response
Vanni & Rosenström, in preparation
VOIcentre
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Active voxels for centre are suppressed during simultaneous presentation
Vanni & Rosenström, in preparation
VOIcentre
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Suppression (red) is surrounded by facilitation (blue)
Vanni & Rosenström, in preparation
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Efficient coding
Response to stimulus A, A’
Res
pons
e to
sti
mul
us B
, B’
A’ = A – dBB’ = 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.
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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.
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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
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Collaborators
• Aalto UniversityLinda HenrikssonLauri NurminenTom Rosenström
• University of HelsinkiJarmo HurriAapo HyvärinenMarkku KilpeläinenPentti Laurinen
• ANU, CanberraAndrew James