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

TL;DR

This paper demonstrates that the Tempotron neural network model, accelerated with GPU, effectively performs pulse shape discrimination without manual feature extraction, achieving high accuracy and significantly faster processing times.

Contribution

The study introduces GPU-accelerated Tempotron for pulse discrimination, highlighting its high accuracy, speed, and insights into its learning process, with publicly available code and datasets.

Findings

GPU acceleration yields over 500x speedup

Tempotron achieves high discrimination accuracy

Analysis of neural activity informs hyperparameter selection

Abstract

This study utilized the Tempotron, a robust 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 prior knowledge. The study performed experiments using GPU acceleration, resulting in over 500 times faster compared to the CPU-based model, and investigated the impact of noise augmentation on the Tempotron performance. Experimental results substantiated that Tempotron serves as a formidable classifier, adept at accomplishing high discrimination accuracy on both AmBe and time-of-flight PuBe datasets. Furthermore, analyzing the neural activity of Tempotron during training shed light on its learning characteristics and aided in selecting its hyperparameters. Moreover, the study addressed the constraints and potential avenues for future development in utilizing the Tempotron for pulse shape discrimination. The dataset used in this study and the GPU-based Tempotron are publicly available on GitHub at https://github.com/HaoranLiu507/TempotronGPU.