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Neural parallel Engine: A toolbox for massively parallel neural signal processing

JOURNAL OF NEUROSCIENCE METHODS 2018年 301卷 18-33页

作者： Tam, Wing-kin Yang, Zhi Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Singapore Singapore Univ Minnesota Twin Cities Dept Biomed Engn Minneapolis MN 55455 USA

Background: Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. New method: In this study, we report the development of Neural parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (CPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Results: Our toolbox is able to offer a 5x to 110x speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Comparison with existing methods: Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. Conclusions: A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. (C) 2018 Elsevier B.V. All rights reserved.

关键词： massively parallel signal processing Neural signal processing Spike detection Spike sorting

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