The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.
Aalto

Computational Models Relating Properties of Visual Neurons to Natural Stimulus Statistics

Jarmo Hurri

Dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the Department of Computer Science and Engineering for public examination and debate in Auditorium T2 at Helsinki University of Technology (Espoo, Finland) on the 5th of December, 2003, at 12 o'clock noon.

Overview in PDF format (ISBN 951-22-6823-X)   [735 KB]
Dissertation is also available in print (ISBN 951-22-6822-1)

Abstract

The topic of this thesis is mathematical modeling of computations taking place in the visual system, the largest sensory system in the primate brain. While a great deal is known about how certain visual neurons respond to stimuli, a very profound question is why they respond as they do. Here this question is approached by formulating models of computation which might underlie the observed response properties. The main motivation is to improve our understanding of how the brain functions. A better understanding of the computational underpinnings of the visual system may also yield advances in medical technology or computer vision, such as development of visual prostheses, or design of computer vision algorithms.

In this thesis several models of computation are examined. An underlying assumption in this work is that the statistical properties of visual stimuli are related to the structure of the visual system. The relationship has formed through the mechanisms of evolution and development. A model of computation specifies this relationship between the visual system and stimulus statistics. Such a model also contains free parameters which correspond to properties of visual neurons. The experimental evaluation of a model consists of estimation of these parameters from a large amount of natural visual data, and comparison of the resulting parameter values against neurophysiological knowledge of the properties of the neurons, or results obtained with other models.

The main contribution of this thesis is the introduction of new models of computation in the primary visual cortex. The results obtained with these models suggest that one defining feature of the computations performed by a class of neurons called simple cells, is that the output of a neuron consists of periods of intense neuronal activity. It also seems that the activity levels of nearby simple cells are positively correlated over short time intervals. In addition, the probability of the occurrence of such regions of intense activity in the joint space of time and cortical area seems to be small. Another contribution of the thesis is the examination of the relationship between two previous computational models, namely independent component analysis and local spatial frequency analysis. This examination suggests that results obtained with independent component analysis share some important properties with wavelets, in the way their localization in space and frequency depends on their average spatial frequency.

This thesis consists of an overview and of the following 7 publications:

  1. Hurri J., Hyvärinen A. and Oja E., 1997. Wavelets and natural image statistics. In: Frydrych M., Parkkinen J. and Visa A. (editors), Proceedings of the 10th Scandinavian Conference on Image Analysis, pages 13-18. © 1997 Pattern Recognition Society of Finland. By permission.
  2. Hurri J. and Hyvärinen A., 2003. Simple-cell-like receptive fields maximize temporal coherence in natural video. Neural Computation 15, number 3, pages 663-691. © 2003 MIT Press. By permission.
  3. Hurri J. and Hyvärinen A., 2002. A novel temporal generative model of natural video as an internal model in early vision. In: Pece A. E. C. (editor), Proceedings of the First International Workshop on Generative-Model-Based Vision (GMBV 2002), pages 33-38. DIKU Technical Report no. 2002/01. © 2002 University of Copenhagen, Department of Computer Science (DIKU). By permission.
  4. Hurri J. and Hyvärinen A., 2003. Temporal and spatiotemporal coherence in simple-cell responses: a generative model of natural image sequences. Network: Computation in Neural Systems 14, number 3, pages 527-551. © 2003 Institute of Physics Publishing Ltd. By permission.
  5. Hurri J. and Hyvärinen A., 2003. Temporal coherence, natural image sequences, and the visual cortex. In: Becker S., Thrun S. and Obermayer K. (editors), Advances in Neural Information Processing Systems 15, pages 141-148. © 2003 MIT Press. By permission.
  6. Hurri J., Väyrynen J. and Hyvärinen A., Spatiotemporal linear simple-cell models based on temporal coherence and independent component analysis. Proceedings of the Eighth Neural Computation and Psychology Workshop, in press.
  7. Hyvärinen A., Hurri J. and Väyrynen J., 2003. Bubbles: a unifying framework for low-level statistical properties of natural image sequences. Journal of the Optical Society of America A 20, number 7, pages 1237-1252. © 2003 Optical Society of America (OSA). By permission.

Keywords: computational neuroscience, cortical coding, cortical topography, primary visual cortex, simple cells, complex cells, temporal coherence, bubble coding, burst firing, independent component analysis, sparse coding

This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.

© 2003 Helsinki University of Technology

Last update 2011-05-26