Bayesian inference & visual processing in the brain

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Presentation transcript:

Bayesian inference & visual processing in the brain Neuroinformatics Group: Aapo Hyvärinen Patrik Hoyer Jarmo Hurri Mika Inki Urs Köster Jussi Lindgren Jukka Perkiö Ilmari Kurki

Paradoxes in perception Perception seems - effortless - straightforward - objective

Paradoxes in perception Perception seems - effortless - straightforward - objective In reality - it cannot be easily programmed in a computer - it seems to require complicated processing - it can be fooled

Example: Illusory motion

Example: Illusory motion

Example 2: completion

Example 2: completion = +

Example 2: completion (not:) NOT: = = +

Example 3: illusory contours

Visual processing as inference Dominant school in vision research: constructivism Perception is unconscious inference Combine Hidden assumptions (priors) given by internal models Incoming sensory information to reach conclusions about the environment. (Helmholtz, late 19th century) Formalized as Bayesian inference

Our approach: Linear models of natural images = S + S + ... + S 1 2 N What are the best linear features for natural images?

Independent component analysis Linear mixtures of source signals: can we find the original ones?

Independent component analysis

Independent component analysis of natural images Low-level statistical prior Similar to what is found in the visual brain areas