Adaptive resonance theory (ART)
The handbook of brain theory and neural networks
Self-Organizing Maps
A Learning Rule to Model the Development of Orientation Selectivity in Visual Cortex
Neural Processing Letters
Hierarchial self-organization of minicolumnar receptive fields
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Topographic Product Models Applied to Natural Scene Statistics
Neural Computation
Dynamics of cortical columns – sensitive decision making
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Dynamics of cortical columns – self-organization of receptive fields
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
Rapid convergence to feature layer correspondences
Neural Computation
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We present a dynamical model of processing and learning in the visual cortex, which reflects the anatomy of V1 cortical columns and properties of their neuronal receptive fields (RFs). The model is described by a set of coupled differential equations and learns by self-organizing the RFs of its computational units - sub-populations of excitatory neurons. If natural image patches are presented as input, self-organization results in Gabor-like RFs. In quantitative comparison with in vivo measurements, we find that these RFs capture statistical properties of V1 simple-cells that learning algorithms such as ICA and sparse coding fail to reproduce.