ICANN 2001: Artificial Neural Networks — ICANN 2001 pp 1075-1080 | Cite as
Extracting Slow Subspaces from Natural Videos Leads to Complex Cells
Conference paper
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Abstract
Natural videos obtained from a camera mounted on a catś 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.
Keywords
Computational Neuroscience Learning Temporal SmothnessPreview
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