Unsupervised STDP-based Radioisotope Identification Using Spiking Neural Networks Implemented on SpiNNaker

Abstract

This paper presents a spiking neural network (SNN) implementation which employs unsupervised feature extraction using spike timing dependent plasticity (STDP) to classify 8 different radioisotopes. With the implementation, the accuracy could reach 80% during training and overall testing accuracy of 72%. The whole network was implemented on SpiNNaker, a spiking neural network emulation platform. This work shows that unsupervised STDP, an SNN native training method, can be applied to the classification task of RIID to provide event-based training as well as inference.