Algorithms and architectures for low power spike detection and alignment

Journal of Neural Engineering
Alex ZviagintsevRan Ginosar

Abstract

We introduce algorithms and architectures for automatic spike detection and alignment that are designed for low power. Some of the algorithms are based on principal component analysis (PCA). Others employ a novel integral transform analysis and achieve 99% of the precision of a PCA detector, while requiring only 0.05% of the computational complexity. The algorithms execute autonomously, but require off-line training and setting of computational parameters. We employ pre-recorded neuronal signals to evaluate the accuracy of the proposed algorithms and architectures: the recorded data are processed by a standard PCA spike detection and alignment software algorithm, as well as by the several hardware algorithms, and the outcomes are compared.

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Citations

May 30, 2008·Journal of Neuroscience Methods·Hsiao-Lung ChanPei-Kuang Chao
Aug 18, 2012·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Awais M Kamboh, Andrew J Mason
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Jun 8, 2010·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Sarah GibsonDejan Marković
Feb 23, 2021·Journal of Neuroscience Methods·Zheng Zhang, Timothy G Constandinou

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