Handwritten digit recognition by neural `gas´ model and population decoding

In this paper, we present a handwritten digit recognition scheme using a topology representation model called neural gas. Instead of applying the model only for feature extraction, we train test separate gas models which aim to describe the data submanifolds respectively in the ten classes. A modular classification system is proposed based on the idea of population decoding, as a topographic map essentially provide a kind of population code for the input. Experiment results show a fast learning and a high recognition rate