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Computational Vision & Neuroscience Group

Using methods of statistical inference and learning theory, as well as signal processing, nonlinear dynamics and optimization theory, our research addresses the problem of perceptual inference from natural images and its neural basis at different levels:

  • We develop mathematical generative models of natural images and image transformations using unsupervised learning methods. Particular emphasis is placed on quantitative comparisons of the performance of these models.
  • We perform psychophysical studies in order to evaluate the relationship between natural image models and perception.
  • We develop new efficient methods to predict the spike trains of neurons in response to natural stimuli with the goal of inferring the contribution of these neurons to the image processing performed in the early visual system. In particular, we build population response models for multi-cell recordings and we address the aspects of contrast adaptation, non-Gaussian stimuli, and inter-spike correlations.

Group Leader

Matthias Bethge
[more information]

Contact

Werner-Reichardt-Centrum fuer Integrative Neurowissenschaften (CIN)

Otfried-Mueller-Str. 25
72076 Tuebingen
Germany

Phone: 0049 (0) 7071 29-89017
Fax: 0049 (0) 7071 29-25015

E-mail: matthias.bethge[at]cin.uni-tuebingen.de

Website: bethgelab.org

 

Methodology

  • Methods of statistical inference and learning theory, as well as signal processing, nonlinear dynamics and optimization theory
  • Psychophysics

Scientific Cooperations

  • Cooperation 1
    Prof. Dr. Tolias (BCM, Texas, USA)
  • Cooperation 2
    Prof. Dr. Eero Simoncelli (NYU, New York, USA)
  • Cooperation 3
    Prof. Dr. Thomas Euler (University of Tübingen)
  • Cooperation 4
    Prof. Dr. Felix Wichmann (TU Berlin)
  • Cooperation 5
    Prof. Dr. Leonard White, (Duke University, North Carolina, USA)

Current Research Projects

  • Project 1
    Natural image modelling  (current focus on hierarchical models)
  • Project 2
    Retinal information processing
  • Project 3
    Population coding
  • Project 4
    Psychophysical testing of natural image models

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