A self-organizing neural network model for the simultaneous and cooperative development of topographic receptive fields and lateral interactions in cortical maps is presented. Both afferent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. Afferent input weights of each neuron self-organize into hill-shaped profiles, receptive fields organize topographically across the network, and unique lateral interaction profiles develop for each neuron. The model demonstrates how patterned lateral connections develop based on correlated activity, and explains why lateral connection patterns closely follow receptive field properties such as ocular dominance.

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Neural Computation, Vol. 9 (1996), pp. 577-594.

@Article{sirosh:neuralcomp, title={Topographic Receptive Fields and Patterned Lateral Interaction in a Self-Organizing Model of the Primary Visual Cortex}, author={Joseph Sirosh and Risto Miikkulainen}, volume={9}, journal={Neural Computation}, pages={577-594}, url="http://www.cs.utexas.edu/users/ai-lab?sirosh:nc97", year={1996} }