Learning the Nonlinearity of Neurons from Natural Visual Stimuli

DOI10.1162/08997660360675026zbMath1054.92012OpenAlexW2106451016WikidataQ52008807 ScholiaQ52008807MaRDI QIDQ4814195

Konrad P. Körding, Christoph Kayser, Peter König

Publication date: 7 September 2004

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/08997660360675026

Mathematics Subject Classification ID

Neural biology (92C20) Psychophysics and psychophysiology; perception (91E30)

Related Items (5)

Energy levels of Moiré patterns: relation to human perception ⋮ Learning Visual Spatial Pooling by Strong PCA Dimension Reduction ⋮ Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images ⋮ Learning viewpoint invariant object representations using a temporal coherence principle ⋮ A Maximum-Likelihood Interpretation for Slow Feature Analysis

Cites Work

• Information Processing in Dendritic Trees
• Slow Feature Analysis: Unsupervised Learning of Invariances
• The statistics of natural images
• Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer
• Statistics of natural time-varying images

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