Pulse shape discrimination based on the Tempotron: a powerful classifier
  on GPU
Haoran Liu Peng Li Ming-Zhe Liu Kai-Ming Wang Zhuo Zuo Bing-Qi Liu
This study introduces the Tempotron, a powerful classifier based on a
third-generation neural network model, for pulse shape discrimination. By
eliminating the need for manual feature extraction, the Tempotron model can
process pulse signals directly, generating discrimination results based on
learned prior knowledge. The study performed experiments using GPU
acceleration, resulting in over a 500 times speedup compared to the CPU-based
model, and investigated the impact of noise augmentation on the Tempotron's
performance. Experimental results showed that the Tempotron is a potent
classifier capable of achieving high discrimination accuracy. Furthermore,
analyzing the neural activity of Tempotron during training shed light on its
learning characteristics and aided in selecting the Tempotron's
hyperparameters. The dataset used in this study and the source code of the
GPU-based Tempotron are publicly available on GitHub at
https://github.com/HaoranLiu507/TempotronGPU.