Date：2005/10/05(Wed）
Venue：IIS, University of Tokyo, D block 3F
Invited Speaker：Prof. Aapo Hyvarinen（(http://www.cs.helsinki.fi/u/ahyvarin/)：senior
researcher at the University of Helsinki with over hundred publications
on statistical learning, machine learning, and computational
neuroscience.

Title: Statistical structure of natural images and cortical visual 
representation

Abstract: A fundamental question in 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.