Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
A Bayesian analysis of self-organizing maps
Neural Computation
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
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In the literature on topographic models of cortical organization, Kohonen's self-organizing map is often treated as a computational shortcut version of a more detailed biological architecture, in which competition in the map is regulated by excitatory and inhibitory lateral interactions. A novel lateral interaction model will be presented here, whose investigation will show: first, that the behavior of the two models is not identical; and second, that the lateral interaction architecture behaves similarly to non-topographic algorithms, constructing representations of the input at intermediate levels of detail in the initial phases of training. This observation supports a novel interpretation of the topographic organization of the cerebral cortex.