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This study introduces the Tempotron, a powerful classifier based on athird-generation neural network model, for pulse shape discrimination. Byeliminating the need for manual feature extraction, the Tempotron model canprocess pulse signals directly, generating discrimination results based onlearned prior knowledge. The study performed experiments using GPUacceleration, resulting in over a 500 times speedup compared to the CPU-basedmodel, and investigated the impact of noise augmentation on the Tempotron'sperformance. Experimental results showed that the Tempotron is a potentclassifier capable of achieving high discrimination accuracy. Furthermore,analyzing the neural activity of Tempotron during training shed light on itslearning characteristics and aided in selecting the Tempotron'shyperparameters. The dataset used in this study and the source code of theGPU-based Tempotron are publicly available on GitHub athttps://github.com/HaoranLiu507/TempotronGPU.

Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU