A Uni ed Neural Network Model for the Self-organization of Topographic Receptive Fields and Lateral Interaction

A self-organizing neural network model for the simultaneous development of topographic receptive elds and lateral interactions in cortical maps is presented. Both aaerent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. AAerent input weights of each neuron self-organize into hill-shaped prooles, receptive elds organize topographically across the network, and unique lateral interaction prooles develop for each neuron. The resulting self-organized structure remains in a dynamic and continuously-adapting equilibrium with the input. The model can be seen as a generalization of previous self-organizing models of the visual cortex, and provides a general computational framework for experiments on receptive eld development and cortical plasticity. The model also serves to point out general limits on activity-dependent self-organization: when multiple inputs are presented simultaneously, the receptive eld centers need to be initially ordered for stable self-organization to occur.