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In this paper, a self-developing neural network model, namely the Growing Cell Structures (GCS) is characterized. In GCS each node (or cell) is associated with a local resource counter τ (t). We show that GCS has the conservation property by which the summation of all resource counters always equals $$\frac{{s(1 - \alpha )}}{\alpha }$$ , thereby s is the increment added to τ (t) of the wining...
In a recent publication [1], it was shown that a biologically plausible RCN (Representation-burden Conservation Network) in which conservation is achieved by bounding the summed representation-burden of all neurons at constant 1, is effective in learning stationary vector quantization. Based on the conservation principle, a new approach for designing a dynamic RCN for processing both stationary and...
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