Algorithms and architectures for low power spike detection and alignment.

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.

journal_name

J Neural Eng

authors

Zviagintsev A,Perelman Y,Ginosar R

doi

10.1088/1741-2560/3/1/004

subject

Has Abstract

pub_date

2006-03-01 00:00:00

pages

35-42

issue

1

eissn

1741-2560

issn

1741-2552

pii

S1741-2560(06)06612-2

journal_volume

3

pub_type

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