Acknowledgements

For those using Easy Electrophysiology in published research, our RRID is RRID:SCR_021190.

Neo

Easy Electrophysiology uses Neo (Garcia et al. 2014), a data-loading package that handles many electrophysiological filetypes.

We also thank the contributing authors of the relevant Neo submodules including Forrest Collman, Harald Hentschke (Axon files) and Jeffrey Gill (AxoGraph files).

Events Template Matching

The template matching event detection algorithm with a biexponential template and correlation coefficient threshold was first described by Jonas et al. (1993).

The Detection Criterion thresholding method was introduced by Clements and Bekkers (1997).

The deconvolution method for template event detection was introduced by Pernia-Andrade et al. (2012).

Action potential threshold detection

Easy Electrophysiology provides a number of options for threshold estimation in Action Potential Kinetics analysis, including Method I and II from Sekerli et al. (2004) and maximum curvature threshold detection from Rossokhin and Saakian (1992).

Spike Frequency Accommodation – Local Variance

The Local Variance measure for spike frequency accommodation is from Shinomoto et al. (2003).

LogISI

The logISI method was introduced and extensively detailed by Pasquale et al. (2010).

PyQtGraph

We would also like to thank Luke Campagnola and the PyQtGraph team for their work on the excellent PyQtGraph package that provides high performance, flexible graphing.

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Pernia-Andrade, A., S. P. Goswami, Y. Stickler, U. Fröbe, A. Schlögl, and P. Jonas. 2012. “A Deconvolution-Based Method with High Sensitivity and Temporal Resolution for Detection of Spontaneous Synaptic Currents in Vitro and in Vivo.” Biophysical Journal 103 (7): 1429–39.

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Sekerli, M., C. A. Del Negro, R. H. Lee, and R. J. Butera. 2004. “Estimating Action Potential Thresholds from Neuronal Time-Series: New Metrics and Evaluation of Methodologies.” IEEE Transactions on Biomedical Engineering 51 (9): 1665–72.

Shinomoto, S., K. Shima, and J. Tanji. 2003. “Differences in Spiking Patterns Among Cortical Neurons.” Neural Computation 15: 2823–42.