Learning invariance from transformation sequences
Neural Computation
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Extracting Slow Subspaces from Natural Videos Leads to Complex Cells
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
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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 statistical time dependencies of image sequences. 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.