The document discusses visual information processing in the brain. It notes that visual cortex neurons are tuned to detect correlated features in our visual environment. These neurons work together to form sparse representations of visual information using independent units. The neurons learn hierarchical models through experience to efficiently encode visual information using surround suppression and other context-dependent mechanisms.

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 1 Neuron 2 Neuron 3 Neuron 4

17. Multiple systems on top of each other Hübener ym, J Neurosci 17 (1997) 9270-9284 Ocular dominance and orientation Spatial 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. Receptive field Angelucci & Bressloff, Prog Brain Res 154 (2006) 93 – 120

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