Yoonsuck Choe, Joseph Sirosh, Risto Miikkulainen
An application of laterally interconnected self-organizing maps (LISSOM) to handwritten digit recognition is presented. The lat(cid:173) eral connections learn the correlations of activity between units on the map. The resulting excitatory connections focus the activity into local patches and the inhibitory connections decorrelate redun(cid:173) dant activity on the map. The map thus forms internal representa(cid:173) tions that are easy to recognize with e.g. a perceptron network. The recognition rate on a subset of NIST database 3 is 4.0% higher with LISSOM than with a regular Self-Organizing Map (SOM) as the front end, and 15.8% higher than recognition of raw input bitmaps directly. These results form a promising starting point for building pattern recognition systems with a LISSOM map as a front end.