Abstract
We apply Slow Feature Analysis (SFA) to image sequences generated from natural images using a range of spatial transformations. An analysis of the resulting receptive fields shows that they have a rich spectrum of invariances and share many properties with complex and hypercomplex cells of the primary visual cortex. Furthermore, the dependence of the solutions on the statistics of the transformations is investigated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Földiák, P.: Learning invariance from transformation sequences. Neural Computation 3 (1991) 194–200
Stone, J.V.: Learning perceptually salient visual parameters using spatiotemporal smoothness constraints. Neural Computation 8 (1996) 1463–1492
Kayser, C., Einhäuser, W., Dümmer, O., König, P., Körding, K.: Extracting slow subspaces from natura1 Videos leads to complex cells. In: Artificial Neural Networks-ICANN 2001 Proceedings, Springer (2001) 1075–1080
Wiskott, L., Sejnowski, T.: Slow feature analysis: Unsupervised learning of invariarnces. Neural Computation 14 (2002) 715–770
Wiskott, L.: Learning invariance manifolds. In: Proc. Computational Neuro-science Meeting, CNS’98, Santa Barbara. (1999) Special issue of Neurocomputing, 26/27:925–932.
van Hateren J.H., van der Schaaf A.: Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Sec. Lond. B (1998) 359–366
Hubel, D., Wiesel, T.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology 160 (1962) 106–154
Mechler, F., Ringach, D.L.: On the classification of simple and complex cells. Vision Research (2002) 1017–1033
Hyvärinnen, A., Hoyer, P.: Emergence of Phase and shift invariant features by decomposition of natura1 images into independent features subspaces. Neural Computation 12 (2000) 1705–1720
De Valois, R., Yund, E., Hepler, N.: The orientation and direction selectivity of cells in macaque visual cortex. Vision Res. 22 (1982) 531–44
De Valois, R., Albrecht, D., Thorell, L.: Spatial frequency selectivity of cells in macaque visual cortex. Vision Res. 22 (1982) 545–559
Schiller, P., Finlay, B., Volman, S.: Quantitative studies of single-cell properties in monkey striate cortex. 1. Spatiotemporal organization of receptive fields. J. Neurophysiol. 39 (1976) 1288–1319
Krüger, N., Peters, G.: Object recognition with banana wavelets. In: Proceedings of the ESANN97. (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berkes, P., Wiskott, L. (2002). Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_14
Download citation
DOI: https://doi.org/10.1007/3-540-46084-5_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44074-1
Online ISBN: 978-3-540-46084-8
eBook Packages: Springer Book Archive