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