Visual cortex

1. 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

2. What is common to subjective
experience, visual perception, and neural
activation?
Statistics of individual visual
environment

3. Sensory and motor areas in human brain
Van Essen (2003) in Visual Neurosciences
27 %
7 % 7 %
8 %

4. Felleman & Van Essen, Cerebral Cortex 1 (1991) 1-47

5. Felleman & Van Essen, Cerebral Cortex 1 (1991) 1-47

6. Mapping of visual cortex
Courtesy of Linda Henriksson

7. Visual information
Correlated features
Sparse coding
Independent representations

8. Visual information
Correlated features
Sparse coding
Independent representations

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.

11. From the eye to the
brain Retina
Thalamus
Cerebral,
cortex

12. Correlated phases at multiple scales
Henriksson, Hyvärinen & Vanni. J Neurosci 29 (2009) 14342-14351

13. Sensitivity to correlated phase
Henriksson, Hyvärinen & Vanni. J Neurosci 29 (2009) 14342-14351

14. Orientation correlations
Geissler et al., Vision Research 41 (2001) 711–724

15. A neuron learns to be selective
Dyan & Abbot: Theoretical Neuroscience (2001) MIT Press

16. Different tuning functions for orientation
Dyan & Abbot: Theoretical Neuroscience (2001) MIT Press
Neuron 1Neuron 2Neuron 3Neuron 4

17. Multiple systems on top of each other
Hübener ym, J Neurosci 17 (1997) 9270-9284
Ocular dominance and orientationSpatial frequency and orientation

18. What is a visual object…

19. http://members.lycos.nl/amazingart/E/20.html

20. Visual information is the regularities of
co-occurence, ”statistics”, of our
environment

21. Visual information
Correlated features
Sparse coding
Independent representations

22. 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

23. Sparse coding
Vinje & Gallant, Science 287 (2000) 1273-1276

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

27. Visual information
Correlated features
Sparse coding
Independent representations

28. Context supports perception

29. Context distorts perception

30. Area tuning function
Varying size
of drifting
gratings
Courtesy of Lauri Nurminen and Markku Kilpeläinen

31. Angelucci & Bressloff, Prog Brain Res 154 (2006) 93 – 120
Receptive field

32. A block model of surround interaction
Schwabe et al. J Neurosci 26 (2006) 9117-9129
Afferent input
Low-level area
High-level area

33. Subtractive normalization model applied
to non-linear interactions in the human
cortex
What visual information has to do
with surround modulation?

34. Stimuli
Vanni & Rosenström, in preparation

35. Centre response covaries with the
surround response
Vanni & Rosenström, in preparation
VOIcentre

36. Active voxels for centre are suppressed
during simultaneous presentation
Vanni & Rosenström, in preparation
VOIcentre

37. Suppression (red) is surrounded by
facilitation (blue)
Vanni & Rosenström, in preparation

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.

39. 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.

40. 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

41. Collaborators
•Aalto University
Linda Henriksson
Lauri Nurminen
Tom Rosenström
•University of Helsinki
Jarmo Hurri
Aapo Hyvärinen
Markku Kilpeläinen
Pentti Laurinen
•ANU, Canberra
Andrew James