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Transcript of Vanni Vipp2010 presentation_bu
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
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 featuresSparse coding
Independent representations
Visual information
Correlated featuresSparse coding
Independent representations
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.
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 featuresSparse 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
Visual information
Correlated featuresSparse coding
Independent representations
Context supports perception
Context distorts perception
Area tuning function
Varying size of drifting gratings
Courtesy of Lauri Nurminen and Markku Kilpeläinen
Angelucci & Bressloff, Prog Brain Res 154 (2006) 93 – 120
Receptive field
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’
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.
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 UniversityLinda HenrikssonLauri NurminenTom Rosenström
• University of HelsinkiJarmo HurriAapo HyvärinenMarkku KilpeläinenPentti Laurinen
• ANU, CanberraAndrew James