Stimfit: quantifying electrophysiological data with Python

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 d...

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Main Authors: Segundo Jose Guzman, Alois eSchlögl, Christoph eSchmidt-Hieber
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-02-01
Series:Frontiers in Neuroinformatics
Subjects:
C++
Online Access:http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00016/full
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spelling doaj-26134f2a12d0483a92d205d40a473ceb2020-11-24T22:01:18ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962014-02-01810.3389/fninf.2014.0001671453Stimfit: quantifying electrophysiological data with PythonSegundo Jose Guzman0Alois eSchlögl1Christoph eSchmidt-Hieber2Christoph eSchmidt-Hieber3Institute of Science and Technology AustriaInstitute of Science and Technology AustriaUniversity College LondonUniversity College LondonIntracellular 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.http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00016/fullElectrophysiologySynaptic Transmissiondata analysispatch-clamppythonC++
collection DOAJ
language English
format Article
sources DOAJ
author Segundo Jose Guzman
Alois eSchlögl
Christoph eSchmidt-Hieber
Christoph eSchmidt-Hieber
spellingShingle Segundo Jose Guzman
Alois eSchlögl
Christoph eSchmidt-Hieber
Christoph eSchmidt-Hieber
Stimfit: quantifying electrophysiological data with Python
Frontiers in Neuroinformatics
Electrophysiology
Synaptic Transmission
data analysis
patch-clamp
python
C++
author_facet Segundo Jose Guzman
Alois eSchlögl
Christoph eSchmidt-Hieber
Christoph eSchmidt-Hieber
author_sort Segundo Jose Guzman
title Stimfit: quantifying electrophysiological data with Python
title_short Stimfit: quantifying electrophysiological data with Python
title_full Stimfit: quantifying electrophysiological data with Python
title_fullStr Stimfit: quantifying electrophysiological data with Python
title_full_unstemmed Stimfit: quantifying electrophysiological data with Python
title_sort stimfit: quantifying electrophysiological data with python
publisher Frontiers Media S.A.
series Frontiers in Neuroinformatics
issn 1662-5196
publishDate 2014-02-01
description 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.
topic Electrophysiology
Synaptic Transmission
data analysis
patch-clamp
python
C++
url http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00016/full
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