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Stimfit: quantifying electrophysiological data with Python.

Guzman, SJ; Schlögl, A; Schmidt-Hieber, C; (2014) Stimfit: quantifying electrophysiological data with Python. Front Neuroinform , 8 , Article 16. 10.3389/fninf.2014.00016. Green open access

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Abstract

Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have developed Stimfit, a free software package for cellular neurophysiology with a Python scripting interface and a built-in Python shell. The program supports most standard file formats for cellular neurophysiology and other biomedical signals through the Biosig library. To quantify and interpret the activity of single neurons and communication between neurons, the program includes algorithms to characterize the kinetics of presynaptic action potentials and postsynaptic currents, estimate latencies between pre- and postsynaptic events, and detect spontaneously occurring events. We validate and benchmark these algorithms, give estimation errors, and provide sample use cases, showing that Stimfit represents an efficient, accessible and extensible way to accurately analyze and interpret neuronal signals.

Type: Article
Title: Stimfit: quantifying electrophysiological data with Python.
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fninf.2014.00016
Publisher version: http://dx.doi.org/10.3389/fninf.2014.00016
Language: English
Additional information: © 2014 Guzman, Schlögl and Schmidt-Hieber. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. PMC ID:PMC3931263
Keywords: C++, Python, biosignal data formats, data analysis, electrophysiology, free software, patch-clamp
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/1420740
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