---
_id: '2230'
abstract:
- lang: eng
  text: 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.
article_number: '16'
author:
- first_name: José
  full_name: Guzmán, José
  id: 30CC5506-F248-11E8-B48F-1D18A9856A87
  last_name: Guzmán
- first_name: Alois
  full_name: Schlögl, Alois
  id: 45BF87EE-F248-11E8-B48F-1D18A9856A87
  last_name: Schlögl
  orcid: 0000-0002-5621-8100
- first_name: Christoph
  full_name: Schmidt Hieber, Christoph
  last_name: Schmidt Hieber
citation:
  ama: 'Guzmán J, Schlögl A, Schmidt Hieber C. Stimfit: Quantifying electrophysiological
    data with Python. <i>Frontiers in Neuroinformatics</i>. 2014;8(FEB). doi:<a href="https://doi.org/10.3389/fninf.2014.00016">10.3389/fninf.2014.00016</a>'
  apa: 'Guzmán, J., Schlögl, A., &#38; Schmidt Hieber, C. (2014). Stimfit: Quantifying
    electrophysiological data with Python. <i>Frontiers in Neuroinformatics</i>. Frontiers
    Research Foundation. <a href="https://doi.org/10.3389/fninf.2014.00016">https://doi.org/10.3389/fninf.2014.00016</a>'
  chicago: 'Guzmán, José, Alois Schlögl, and Christoph Schmidt Hieber. “Stimfit: Quantifying
    Electrophysiological Data with Python.” <i>Frontiers in Neuroinformatics</i>.
    Frontiers Research Foundation, 2014. <a href="https://doi.org/10.3389/fninf.2014.00016">https://doi.org/10.3389/fninf.2014.00016</a>.'
  ieee: 'J. Guzmán, A. Schlögl, and C. Schmidt Hieber, “Stimfit: Quantifying electrophysiological
    data with Python,” <i>Frontiers in Neuroinformatics</i>, vol. 8, no. FEB. Frontiers
    Research Foundation, 2014.'
  ista: 'Guzmán J, Schlögl A, Schmidt Hieber C. 2014. Stimfit: Quantifying electrophysiological
    data with Python. Frontiers in Neuroinformatics. 8(FEB), 16.'
  mla: 'Guzmán, José, et al. “Stimfit: Quantifying Electrophysiological Data with
    Python.” <i>Frontiers in Neuroinformatics</i>, vol. 8, no. FEB, 16, Frontiers
    Research Foundation, 2014, doi:<a href="https://doi.org/10.3389/fninf.2014.00016">10.3389/fninf.2014.00016</a>.'
  short: J. Guzmán, A. Schlögl, C. Schmidt Hieber, Frontiers in Neuroinformatics 8
    (2014).
date_created: 2018-12-11T11:56:27Z
date_published: 2014-02-21T00:00:00Z
date_updated: 2021-01-12T06:56:09Z
day: '21'
ddc:
- '570'
department:
- _id: ScienComp
- _id: PeJo
doi: 10.3389/fninf.2014.00016
file:
- access_level: open_access
  checksum: eeca00bba7232ff7d27db83321f6ea30
  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T10:12:17Z
  date_updated: 2020-07-14T12:45:34Z
  file_id: '4935'
  file_name: IST-2016-425-v1+1_fninf-08-00016.pdf
  file_size: 2883372
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file_date_updated: 2020-07-14T12:45:34Z
has_accepted_license: '1'
intvolume: '         8'
issue: FEB
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
publication: Frontiers in Neuroinformatics
publication_identifier:
  issn:
  - '16625196'
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '4731'
pubrep_id: '425'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Stimfit: Quantifying electrophysiological data with Python'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 8
year: '2014'
...