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

Shouyu Xie, Edward Jones, Edward Marsden, Ian Baistow, Steve Furber, Srinjoy Mitra, Alister Hamilton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication2022 8th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)
PublisherIEEE
ISBN (Electronic)978-1-6654-5349-3
ISBN (Print)978-1-6654-5350-9
DOIs
Publication statusPublished - 18 Aug 2022

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