From spike to graph—A complete automated single-spike analysis

Friedrich, Reut; Ashery, Uri

doi:10.1016/j.jneumeth.2010.09.004

Cited by 7 publications

(12 citation statements)

References 29 publications

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“…In the Mosharov algorithm, the user sets the extent of allowable overlap, defined as the percentage ratio of the minima between adjacent spikes and the maximal height of the taller spike. A similar approach to reject overlapping spikes is described by Friedrich and Ashery (Friedrich and Ashery, 2010). Spikes accepted using an overlap setting of 50% (a typical level, (Mosharov and Sulzer, 2005)) are shown in Fig 5(B), whereas Fig 5(C) show that clearly overlapping spikes are accepted even for an overlap setting as low as 0.5%.…”

Section: Resultsmentioning

confidence: 99%

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

Ramachandran

Gillis

2019

Journal of Neuroscience Methods

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Background: Quantal exocytosis of oxidizable neurotransmitters can be detected as spikes of amperometric current using electrochemical microelectrodes. Measurements of spike parameters indicate the maximal transmitter flux, flux duration, and amount of transmitter released from individual vesicles. Automated analysis algorithms need to reject spikes that overlap in time. In addition, many spikes are preceded by small amplitude “foot” signals, attributed to slow release of transmitter through a fusion pore. Accurate pre-spike baseline determination is essential for estimating fusion-pore duration and the amount of transmitter released through the fusion pore. New Method: We developed an estimation approach that is based on fitting a multi-exponential function to the data. Our previously described matched-filter algorithm is used to identify the sections of data to fit and provides seed values to facilitate convergence of the iterative fit. The new estimation algorithm includes overlap rejection, a two-step fitting procedure and a novel baseline estimation procedure. Results: Histograms of spike parameters demonstrate excellent agreement of the new approach with manually computed parameters. Comparison with Existing Methods: Parameter estimates generated using the new approach are closer to blind manual estimates than commonly used existing methods. The improved performance is due to better detection of valid spikes and rejection of overlapping spikes. Moreover, since the complete time course of the spike is fit to a function, more complete information about the spike time course is captured. Conclusions: The matched-filter seeded algorithm reliably rejects overlaps and estimates spike and foot signal parameters in a fully automated manner.

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Section: Resultsmentioning

confidence: 99%

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

Ramachandran

Gillis

2019

Journal of Neuroscience Methods

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“…An alternative approach to detect exocytotic spikes is to subtract the time-varying baseline from the signal and then detect spikes that exceed amplitude thresholds (Friedrich and Ashery, 2010). However, tracking the time-varying baseline with pA precision requires that the baseline be stable over the averaging time window and relies on user input of the noise level.…”

Section: Discussionmentioning

confidence: 99%

“…However, tracking the time-varying baseline with pA precision requires that the baseline be stable over the averaging time window and relies on user input of the noise level. Also, in an amplitude-based algorithm small-amplitude flickers may be mistaken as spikes (Friedrich and Ashery, 2010) whereas our approach rejects such flickers because their time courses do not match the templates. Avoiding false positives inevitably makes amplitude-based algorithms more biased against small-amplitude events than template-based algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…Detecting when each spike occurs is the first step of analyzing spike parameters and also quantifies the rate of release events as the spike frequency. Manual spike detection in amperometric recordings is labor intensive and therefore automated spike detection software is often used (Friedrich and Ashery, 2010; Mosharov and Sulzer, 2005; Segura et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

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

Ramachandran

Gillis

2018

Journal of Neuroscience Methods

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“…Many aspects of data analysis have undergone a process of automation starting from filters [6] to spike detection [7] and sorting [8]–[10], and from feature analysis of spike responses [11], [12], and inter-burst interval detection [13] in EEG recordings to statistical analysis and visualisation [14].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings

Nowotny

Rospars²,

Martinez

et al. 2013

PLoS ONE

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The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters.

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From spike to graph—A complete automated single-spike analysis

Cited by 7 publications

References 29 publications

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

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

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

Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings

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