Papers on natural image statistics and V1

[Aapo Hyvarinen]

Dear Colleagues,

the following papers are now available on the web.

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J. Hurri and A. Hyvarinen.
Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural
Video. 
Submitted manuscript.
http://www.cis.hut.fi/aapo/ps/gz/Hurri01.ps.gz

Abstract: Recently, statistical models of natural images have shown
emergence of several properties of the visual cortex. Most models have
considered the non-Gaussian properties of static image patches,
leading to sparse coding or independent component analysis. Here we
consider the basic time dependencies of image sequences instead of
their non-Gaussianity.  We show that simple cell type receptive fields
emerge when temporal response strength correlation is maximized for
natural image sequences. Thus, temporal response strength correlation,
which is a nonlinear measure of temporal coherence, provides an
alternative to sparseness in modeling simple cell receptive field
properties. Our results also suggest an interpretation of simple cells
in terms of invariant coding principles that have previously been used
to explain complex cell receptive fields.

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A. Hyvarinen.
An Alternative Approach to Infomax and Independent Component Analysis. 
Neurocomputing, in press (CNS'01). 
http://www.cis.hut.fi/aapo/ps/gz/CNS01.ps.gz

Abstract: Infomax means maximization of information flow in a neural
system.  A nonlinear version of infomax has been shown to be connected
to independent component analysis and the receptive fields of neurons
in the visual cortex.  Here we show a problem of nonrobustness of
nonlinear infomax: it is very sensitive to the choice the nonlinear
neuronal transfer function.  We consider an alternative approach in
which the system is linear, but the noise level depends on the mean of
the signal, as in a Poisson neuron model. This gives similar
predictions as the nonlinear infomax, but seem to be more robust.

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Also, a considerably revised version of a paper that I already
announced on the connectionists list in June 2001: 

P.O. Hoyer and A. Hyvarinen. 
A Multi-Layer Sparse Coding Network Learns Contour Coding from Natural Images. 
Vision Research, in press
http://www.cis.hut.fi/aapo/ps/gz/VR02.ps.gz

Abstract: An important approach in visual neuroscience considers how the
function of the early visual system relates to the statistics of its
natural input. Previous studies have shown how many basic properties
of the primary visual cortex, such as the receptive fields of simple
and complex cells and the spatial organization (topography) of the
cells, can be understood as efficient coding of natural images. Here
we extend the framework by considering how the responses of complex
cells could be sparsely represented by a higher-order neural layer. This
leads to contour coding and end-stopped receptive fields.
In addition, contour integration could be interpreted as top-down 
inference in the presented model.





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Aapo Hyvarinen

Neural Networks Research Centre
Helsinki University of Technology
P.O.Box 9800, FIN-02015 HUT, Finland

Email: Aapo.Hyvarinen at hut.fi
Home page: http://www.cis.hut.fi/aapo/
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