Department of Computer Science, University of Helsinki

Statistical structure of natural images and cortical
visual representations

A fundamental question in theoretical
visual neuroscience is: Why are the receptive fields and
response properties of visual neurons as they are? A modern
approach to this problem emphasizes the importance of adaptation
to ecologically valid input. In this talk I will review work
on modelling statistical regularities in ecologically valid
visual input (``natural images'') and the obtained functional
explanation of the properties of visual neurons. A seminal statistical
model for natural images was linear sparse coding which is
equivalent to the model called independent component analysis
(ICA). Linear features estimated by ICA resemble wavelets or
Gabor functions, and provide a very good description of the
properties of simple cells in the primary visual cortex. We
have introduced extensions of ICA that are based on modelling
dependencies of the "independent" components estimated by basic
ICA. The dependencies of the components are used to define
either a grouping or a topographic order between the components.
With natural image data, these models lead to emergence of
further properties of visual neurons: the topographic organization
and complex cell receptive fields. We
have also modelled the temporal structure of natural image
sequences, which provides an alternative approach to the sparseness
used in most models. Finally, I will discuss a promising new
direction of research: predictive visual neuroscience. There,
the goal is to try to predict response properties of neurons
in areas that are poorly understood, still based on statistical
modelling of natural input.