Implementation of Neural Gas training in analog VLSI

Fabio Ancona, Stefano Rovetta, and Rodolfo Zunino
Department of Biophysical and Electronic Engineering
University of Genova


ABSTRACT

The design and implementation of a vector quantization neural network
is presented.  The training algorithm is Neural Gas.  The
implementation is fully parallel and mainly analog (only control
function and long-term memory are digital). A sequential
implementation of the required sorting function allows to compute the
Neural Gas updating step.



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Proceedings of the 1997 International Symposium on Intelligent Systems
Reggio Calabria, Italy, September 1997, pp. 105-110