Research Group Leader

Prof. Dr. Matthias Bethge
Prof. Dr. Matthias Bethge
Phone: +49 7071 29-89017
Fax: +49 7071 29-25015
mbethge[at]tuebingen.mpg.de

 

Secretary: Heike König
Phone: +49 7071 29-89018
Fax: +49 7071 29-25015
heike.koenig[at]tuebingen.mpg.de
 

 

Sensory Coding and Natural Image Statistics (Winter Term 07/08)
Course Content

Computational theories of the visual brain with testable predictions are scarce. Since it is not known how to design an algorithm that matches the brain’s performance on visual tasks [Pinto et al., 2008], it is difficult to evaluate how much the early stages contribute to solving these tasks. More than 50 years ago, motivated by developments in information theory, Attneave [1954] suggested an intriguing approach to assess the computational role of early vision which is not confined to a particular visual task. Rather, he argued that the overarching goal of perception is to produce an efficient representation of the incoming signal. In a neurobiological context, Barlow [1959] similarly hypothesized that the role of early sensory neurons is to remove statistical redundancy in the sensory input. Variants of this efficient coding hypothesis have been formulated by numerous other authors [e.g. Laughlin, 1981; Foldiak, 1991; Atick, 1992; van Hateren, 1992; Field, 1994; Rieke et al., 1995; Zhaoping, 2006]. For an excellent review, see [Simoncelli and Olshausen, 2001].
This block seminar will provide an introduction to the mathematical fundamentals of unsupervised representation learning and natural image statistics. It is divided into the following five units:
Monday: Generating Random Variables and linear Algebra Tuesday: PCA, Whitening, Fourier and DCT Wednesday: CCA, Amuse , oriented PCA and SOBI Thursday: ICA and Differential Entropy Friday: Discrete Entropy, Minimum mean squared error, and Rate Distortion Theory
Location and Time
University Osnabrück, February 11-15
Lecturer
Dr. Matthias Bethge

Reading


Background:
Feynman (1974) Cargo Cult Science.

Masland and Martin (2007) The Unsolved Mystery of Vision. Current Biology.

Pinto et al (2008) Why is Real World Object Recognition Hard?. PLOS Computational Biology.



Last updated: Wednesday, 27.02.2013