Learning Centre

Computational models relating properties of visual neurons to natural stimulus statistics

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Hurri, Jarmo
dc.date.accessioned 2012-02-10T09:11:07Z
dc.date.available 2012-02-10T09:11:07Z
dc.date.issued 2003-12-05
dc.identifier.isbn 951-22-6823-X
dc.identifier.issn 1459-7020
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2149
dc.description.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. en
dc.format.extent 68, [105]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Dissertations in computer and information science. Report D en
dc.relation.ispartofseries 3 en
dc.relation.haspart 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. [article1.pdf] © 1997 Pattern Recognition Society of Finland. By permission.
dc.relation.haspart 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. [article2.pdf] © 2003 MIT Press. By permission.
dc.relation.haspart 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. [article3.pdf] © 2002 University of Copenhagen, Department of Computer Science (DIKU). By permission.
dc.relation.haspart 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. [article4.pdf] © 2003 Institute of Physics Publishing Ltd. By permission.
dc.relation.haspart 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. [article5.pdf] © 2003 MIT Press. By permission.
dc.relation.haspart 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.
dc.relation.haspart 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. [article7.pdf] © 2003 Optical Society of America (OSA). By permission.
dc.subject.other Computer science en
dc.title Computational models relating properties of visual neurons to natural stimulus statistics en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Computer Science and Engineering en
dc.contributor.department Tietotekniikan osasto fi
dc.subject.keyword computational neuroscience en
dc.subject.keyword cortical coding en
dc.subject.keyword cortical topography en
dc.subject.keyword primary visual cortex en
dc.subject.keyword simple cells en
dc.subject.keyword complex cells en
dc.subject.keyword temporal coherence en
dc.subject.keyword bubble coding en
dc.subject.keyword burst firing en
dc.subject.keyword independent component analysis en
dc.subject.keyword sparse coding en
dc.identifier.urn urn:nbn:fi:tkk-001172
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.lab Laboratory of Computer and Information Science en
dc.contributor.lab Informaatiotekniikan laboratorio fi
local.aalto.digifolder Aalto_64286
local.aalto.digiauth ask


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search archive


Advanced Search

article-iconSubmit a publication

Browse