Spiking Self-Organizing Maps for Classification Problem

In Self-Organizing Maps (SOM) learning, preserving the map topology to simulate the real input features is one of the most important processes. This is done by training the weight values within the Best Matching Unit (BMU) neighborhood. Improper input feeding will cause failure in identifying the potential BMU which will lead to poor map topology. Many studies have been done to optimize the structure of SOM's topology using Artificial Neural Networks (ANN). Spiking Neural Network (SNN) is the third generation of ANN, where information are transferred from one neuron to other using spikes, and processed to trigger response as an output. Current researches have proven that SNN would be an alternative solution for enhancing ANN learning due to its superiority in capturing the internal relationship of neurons. This paper proposes embedded spiking neurons for Kohonen's Self-organizing Maps (SOM) learning to improve its learning process. The proposed Spiking SOM is divided into four main phases. Phase 1 involves the development of the training sample for SOM learning through neural coding schemes. In Phase 2, the spike values are fed into the training process and potential weights are generated. Phase 3 identifies and labels the outputs from the Spiking SOM classification based on the features and characteristics. Finally, in Phase 4, proposed Spiking SOM model is validated using classification accuracy, error quantization and statistical tests using Pearson correlation. Early experiment is conducted using the 1D coding schemes for transforming dataset into spike times with hexagonal lattice structure of SOM network. Result on cancer dataset shows that the tested model has produced feasible classification accuracy with low quantization error. It shows that the 1D coding is capable in preserving the features in the input neurons. (C) 2013 The Authors. Published by Elsevier Ltd.