Literature DB >> 20869399

From spike to graph--a complete automated single-spike analysis.

Reut Friedrich1, Uri Ashery.   

Abstract

Amperometry is a commonly used technique for detecting the kinetics of single-vesicle exocytosis with excellent temporal and spatial resolution. However, different methods of analyzing the amperometric signals can produce conflicting conclusions. We developed an efficient automated method for kinetics analysis of single spikes that does not filter the data and therefore prevents distortion of the results. The algorithm assesses the signal-to-noise ratios (SNRs) and accordingly, separates the signals using an adjustable two-threshold calculation. This enables comparing data with different SNRs from different setups. The software also includes a complete statistical analysis, with an automated selection of the most appropriate statistical tests and a graphical representation. The algorithms can be used for any other experimental results requiring the separation of signals from noise, making this method useful for many applications.
Copyright © 2010 Elsevier B.V. All rights reserved.

Mesh:

Year:  2010        PMID: 20869399     DOI: 10.1016/j.jneumeth.2010.09.004

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  3 in total

1.  Estimating amperometric spike parameters resulting from quantal exocytosis using curve fitting seeded by a matched-filter algorithm.

Authors:  Supriya Balaji Ramachandran; Kevin D Gillis
Journal:  J Neurosci Methods       Date:  2018-09-22       Impact factor: 2.390

2.  A matched-filter algorithm to detect amperometric spikes resulting from quantal secretion.

Authors:  Supriya Balaji Ramachandran; Kevin D Gillis
Journal:  J Neurosci Methods       Date:  2017-10-20       Impact factor: 2.390

3.  Machine learning for automatic prediction of the quality of electrophysiological recordings.

Authors:  Thomas Nowotny; Jean-Pierre Rospars; Dominique Martinez; Shereen Elbanna; Sylvia Anton
Journal:  PLoS One       Date:  2013-12-04       Impact factor: 3.240

  3 in total

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