Learning invariance from transformation sequences
Neural Computation
What is the goal of sensory coding?
Neural Computation
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Learning to Categorize Objects Using Temporal Coherence
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Receptive Fields Similar to Simple Cells Maximize Temporal Coherence in Natural Video
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
An Adaptive Hierarchical Model of the Ventral Visual Pathway Implemented on a Mobile Robot
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Blind separation of sources that have spatiotemporal variance dependencies
Signal Processing - Special issue on independent components analysis and beyond
Slow feature discriminant analysis and its application on handwritten digit recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Nonlinear dimensionality reduction using a temporal coherence principle
Information Sciences: an International Journal
Extracting coactivated features from multiple data sets
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Learning Topographic Representations of Nature Images with Pairwise Cumulant
Neural Processing Letters
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Recently, statistical models of natural images have shown the emergence of several properties of the visual cortex. Most models have considered the nongaussian 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 nongaussianity. 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, which have previously been used to explain complex-cell receptive fields.