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.
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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
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DOI: https://doi.org/10.1007/3-540-46084-5_14
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