A Hybrid Self-organizing Neural Gas Based Network

This paper examines the neural gas networks proposed by Martinetz and Schulten [1] and Fritzke [2] in an effort to create a more biologically plausible hybrid version. The hybrid algorithm proposed in this work retains most of the advantages of the Growing Neural Gas (GNG) algorithm while adapting a reduced parameter and more biologically plausible design. It retains the ability to place nodes where needed, as in the GNG algorithm, without actually having to introduce new nodes. Also, by removing the weight and error adjusting parameters, the guesswork required to determine parameters is eliminated. When compared to Fritzke's algorithm, the hybrid algorithm performs admirably in terms of the quality of results it is slightly slower due to the greater computational overhead. However, it is more biologically feasible and somewhat more flexible due to its hybrid nature and lack of reliance on adjustment parameters.