Publications by topic 
Natural image statistics and the visual cortex[Why are the receptive fields in the visual cortex as they are? The modern answer to this question emphasizes adaptation to the statistical structure of ecologically valid stimuli (natural images). Our work models complex cell receptive fields, topography, and even V2]General
A. Hyvärinen, J. Hurri, and P. O. Hoyer. Natural Image Statistics. SpringerVerlag, 2009.
A. Hyvärinen Statistical models of natural images and cortical visual representation.
Topics in Cognitive Science 2:251264, 2010.
Beyond primary visual cortex
H. Hosoya and A. Hyvärinen.
A mixture of sparse coding models explaining properties of face neurons related to holistic and partsbased processing. PLoS Computational Biology, 2017.
H. Hosoya and A. Hyvärinen.
A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2. J. of Neuroscience, 35:1041210428, 2015.
M. U. Gutmann and A. Hyvärinen. A Threelayer Model of Natural Image Statistics.
J. of Physiology (Paris), 107:369398, 2013.
H. Sasaki, M. U. Gutmann, H. Shouno and A. Hyvärinen
Correlated Topographic Analysis: Estimating an Ordering of Correlated Components. Machine Learning, 92:285317, 2013.
H. Sasaki, M. U. Gutmann, H. Shouno and A. Hyvärinen
Simultaneous Estimation of NonGaussian
Components and their Correlation Structure. Neural Computation, 29:28872924, 2017. A. Hyvärinen, M. Gutmann and P.O. Hoyer. Statistical model of natural stimuli predicts edgelike pooling of spatial frequency channels in V2.
BMC Neuroscience, 6:12, 2005.
P.O. Hoyer and A. Hyvärinen. A MultiLayer Sparse Coding Network Learns Contour Coding from Natural Images.
Vision Research, 42(12):15931605, 2002.
J.T. Lindgren, J. Hurri and A. Hyvärinen.Spatial dependencies between local luminance and contrast in natural images. Journal of Vision 8(12)113, 2008
J.T. Lindgren and A. Hyvärinen.On the Learning of Nonlinear Visual Features from Natural Images by Optimizing Response Energies. Proc. Int. Joint Conf. on Neural Networks (IJCNN2008), Hong Kong, 2008.
J.T. Lindgren and A. Hyvärinen. Emergence of conjunctive
visual features by quadratic independent component analysis.
Advances in Neural Information Processing Systems (NIPS2006),
J.T. Lindgren and A. Hyvärinen. Learning highlevel independent components of images through a spectral representation.
Proc. Int. Conf. on Pattern Recognition (ICPR2004), pp. 7275, Cambridge, UK, 2004.
Sparse coding / ICA models of V1
H. Hosoya and A. Hyvärinen.
Learning Visual Spatial Pooling by Strong PCA Dimension Reduction. Neural Computation, 28:12491264, 2016.
M. U. Gutmann, V. Laparra, A. Hyvärinen and J. Malo.
SpatioChromatic Adaptation via HigherOrder Canonical Correlation Analysis of Natural Images
. PLoS ONE, 9(2):e86481, 2014.
V. Laparra, M. U. Gutmann, J. Malo and A. Hyvärinen.
Complexvalued independent component analysis of natural images
. Proc. Int. Conf. on Artificial Neural Networks (ICANN2011), Helsinki, Finland, 2011.
U. Köster, J. Lindgren, M. Gutmann and A. Hyvärinen.
Learning Natural Image Structure with a Horizontal Product Model. Proc. Int. Conf. on Independent Component Analysis and Blind Source Separation (ICA2009), Paraty, Brazil, 2009.
U. Köster, J. Lindgren and A. Hyvärinen.
Estimating Markov Random Field Potentials for Natural Images. Proc. Int. Conf. on Independent Component Analysis and Blind Source Separation (ICA2009), Paraty, Brazil, 2009.
U. Köster and A. Hyvärinen. A TwoLayer Model of Natural Stimuli Estimated with Score Matching, Neural Computation, 22:23082333, 2010.
A. Hyvärinen and P. O. Hoyer.
A TwoLayer Sparse Coding Model Learns Simple and Complex
Cell Receptive Fields and Topography from Natural Images.
Vision Research, 41(18):24132423, 2001.
A. Hyvärinen, P.O. Hoyer and M. Inki. Topographic Independent
Component Analysis. Neural Computation, 13(7):15271558, 2001.
A. Hyvärinen and P.O. Hoyer. Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces.
Neural Computation, 12(7):17051720, 2000.
A. Hyvärinen and P. O. Hoyer.
Emergence of Topography and Complex Cell Properties
from Natural Images using Extensions of ICA.
Advances in Neural Information Processing Systems 12 (NIPS*99), pp. 827833, 2000.
P.O. Hoyer and A. Hyvärinen. Independent Component Analysis Applied to Feature Extraction from Colour and Stereo Images.
Network: Computation in Neural Systems, 11(3):191210, 2000.
A. Hyvärinen.
An alternative approach to infomax and independent component analysis
. Neurocomputing, 4446(C):10891097, 2002.
A. Hyvärinen and U. Köster. Complex Cell Pooling and
the Statistics of Natural Images . Network: Computation in Neural
Systems, 18:81100, 2007. MATLAB code for estimating ICA, ISA, and TICA bases from image data is also available (by P. O. Hoyer). Temporal coherence models of V1
J. Hurri and A. Hyvärinen. SimpleCellLike Receptive Fields Maximize Temporal Coherence in Natural Video.
Neural Computation, 15(3):663691, 2003.
J. Hurri and A. Hyvärinen.Temporal and spatiotemporal coherence in simplecell responses: A generative model of natural image sequences, Network: Computation in Neural Systems, 14(3):527551, 2003 (special issue on sensory coding and natural stimuli).
J. Hurri and A. Hyvärinen.Temporal Coherence, Natural Image Sequences, and the Visual Cortex.
Advances in Neural Information Processing Systems 15 (NIPS*02), MIT Press, pp. 141148, 2003.
A. Hyvärinen, J. Hurri, and J. Väyrynen. Bubbles: a unifying framework for lowlevel statistical properties of natural image sequences.
Journal of the Optical Society of America A, 20(7):12371252, 2003 (Special Issue on Bayesian and statistical approaches to vision).
