Hierarchial self-organization of minicolumnar receptive fields
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Dynamics of cortical columns – sensitive decision making
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
Learning of Neural Information Routing for Correspondence Finding
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Goal-directed feature learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A dynamical model for receptive field self-organization in V1 cortical columns
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Learning of lateral connections for representational invariant recognition
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Dynamics of cortical columns – sensitive decision making
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Rapid correspondence finding in networks of cortical columns
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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We present a system of differential equations which abstractly models neural dynamics and synaptic plasticity of a cortical macrocolumn. The equations assume inhibitory coupling between minicolumn activities and Hebbian type synaptic plasticity of afferents to the minicolumns. If input in the form of activity patterns is presented, self-organization of receptive fields (RFs) of the minicolumns is induced. Self-organization is shown to appropriately classify input patterns or to extract basic constituents form input patterns consisting of superpositions of subpatterns. The latter is demonstrated using the bars benchmark test. The dynamics was motivated by the more explicit model suggested in [1] but represents a much compacter, continuous, and easier to analyze dynamic description.