Connectionist learning procedures
Artificial Intelligence
Learning invariance from transformation sequences
Neural Computation
Receptive Fields Similar to Simple Cells Maximize Temporal Coherence in Natural Video
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Learning Multiple Feature Representations from Natural Image Sequences
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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
Invariant Object Recognition with Slow Feature Analysis
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Nonlinear dimensionality reduction using a temporal coherence principle
Information Sciences: an International Journal
Invariant object recognition and pose estimation with slow feature analysis
Neural Computation
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Natural videos obtained from a camera mounted on a cat's head are used as stimuli for a network of subspace energy detectors. The network is trained by gradient ascent on an objective function defined by the squared temporal derivatives of the cells' outputs. The resulting receptive fields are invariant to both contrast polarity and translation and thus resemble complex type receptive fields.