Algorithms and architectures for low power spike detection and alignment.

Alex Zviagintsev; Yevgeny Perelman; Ran Ginosar

doi:10.1088/1741-2560/3/1/004

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Algorithms and architectures for low power spike detection and alignment.

Alex Zviagintsev·Yevgeny Perelman·Ran Ginosar

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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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Alex Zviagintsev, Yevgeny Perelman, Ran Ginosar. 2006-01-20. Algorithms and architectures for low power spike detection and alignment.. https://doi.org/10.1088/1741-2560%2F3%2F1%2F004

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