SPIGEN: A Configurable Population-Coded Spike Pattern Generator for Single-Spike Learning in Spiking Neural Networks

This study introduces SPIGEN, a configurable spike pattern generator designed to convert continuous-valued input features into fixed-length binary spike representations suitable for spiking neural networks. Based on a population coding framework, SPIGEN enables flexible configuration through multiple quantization and encoding schemes. Its primary goal is to transform continuous features into time-independent spike patterns that preserve inter-class separability and intra-class consistency. The study includes two experimental stages to evaluate the effectiveness of the proposed method. First, SPIGEN was used to generate spike patterns from two benchmark datasets—IRIS and Digits—and a synthetically generated Gaussian dataset. The resulting spike patterns were classified using conventional machine learning algorithms and a single-layer perceptron to examine how well the transformation process retained the information content of the original features. The results indicated that SPIGEN patterns yielded classification performances comparable to the original features, and the choice of encoding configuration significantly influenced the outcome. In the second stage, the generated spike patterns were applied as synaptic inputs to biologically inspired neuron models, where '1' indicates a spike and '0' denotes silence. When both excitatory and inhibitory synapses were used during training, the evaluation results suggested that SPIGEN is a viable solution for representing continuous input features in biologically plausible spiking neural networks.