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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Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00016/full |
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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 |
work_keys_str_mv |
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