---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '15169'
abstract:
- lang: eng
  text: Interpretation of extracellular recordings can be challenging due to the long
    range of electric field. This challenge can be mitigated by estimating the current
    source density (CSD). Here we introduce kCSD-python, an open Python package implementing
    Kernel Current Source Density (kCSD) method and related tools to facilitate CSD
    analysis of experimental data and the interpretation of results. We show how to
    counter the limitations imposed by noise and assumptions in the method itself.
    kCSD-python allows CSD estimation for an arbitrary distribution of electrodes
    in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in
    a single cell, and includes a range of diagnostic aids. We demonstrate its features
    in a Jupyter Notebook tutorial which illustrates a typical analytical workflow
    and main functionalities useful in validating analysis results.
acknowledgement: 'The Python implementation of kCSD was started by Grzegorz Parka
  during Google Summer of Code project through the International Neuroinformatics
  Coordinating Facility. Jan Mąka implemented the first Python version of skCSD class.
  This work was supported by the Polish National Science Centre (2013/08/W/NZ4/00691
  to DKW; 2015/17/B/ST7/04123 to DKW). '
article_number: e1011941
article_processing_charge: Yes
article_type: original
author:
- first_name: Chaitanya
  full_name: Chintaluri, Chaitanya
  id: E4EDB536-3485-11EA-98D2-20AF3DDC885E
  last_name: Chintaluri
- first_name: Marta
  full_name: Bejtka, Marta
  last_name: Bejtka
- first_name: Wladyslaw
  full_name: Sredniawa, Wladyslaw
  last_name: Sredniawa
- first_name: Michal
  full_name: Czerwinski, Michal
  last_name: Czerwinski
- first_name: Jakub M.
  full_name: Dzik, Jakub M.
  last_name: Dzik
- first_name: Joanna
  full_name: Jedrzejewska-Szmek, Joanna
  last_name: Jedrzejewska-Szmek
- first_name: Daniel K.
  full_name: Wojciki, Daniel K.
  last_name: Wojciki
citation:
  ama: Chintaluri C, Bejtka M, Sredniawa W, et al. kCSD-python, reliable current source
    density estimation with quality control. <i>PLoS Computational Biology</i>. 2024;20(3).
    doi:<a href="https://doi.org/10.1371/journal.pcbi.1011941">10.1371/journal.pcbi.1011941</a>
  apa: Chintaluri, C., Bejtka, M., Sredniawa, W., Czerwinski, M., Dzik, J. M., Jedrzejewska-Szmek,
    J., &#38; Wojciki, D. K. (2024). kCSD-python, reliable current source density
    estimation with quality control. <i>PLoS Computational Biology</i>. Public Library
    of Science. <a href="https://doi.org/10.1371/journal.pcbi.1011941">https://doi.org/10.1371/journal.pcbi.1011941</a>
  chicago: Chintaluri, Chaitanya, Marta Bejtka, Wladyslaw Sredniawa, Michal Czerwinski,
    Jakub M. Dzik, Joanna Jedrzejewska-Szmek, and Daniel K. Wojciki. “KCSD-Python,
    Reliable Current Source Density Estimation with Quality Control.” <i>PLoS Computational
    Biology</i>. Public Library of Science, 2024. <a href="https://doi.org/10.1371/journal.pcbi.1011941">https://doi.org/10.1371/journal.pcbi.1011941</a>.
  ieee: C. Chintaluri <i>et al.</i>, “kCSD-python, reliable current source density
    estimation with quality control,” <i>PLoS Computational Biology</i>, vol. 20,
    no. 3. Public Library of Science, 2024.
  ista: Chintaluri C, Bejtka M, Sredniawa W, Czerwinski M, Dzik JM, Jedrzejewska-Szmek
    J, Wojciki DK. 2024. kCSD-python, reliable current source density estimation with
    quality control. PLoS Computational Biology. 20(3), e1011941.
  mla: Chintaluri, Chaitanya, et al. “KCSD-Python, Reliable Current Source Density
    Estimation with Quality Control.” <i>PLoS Computational Biology</i>, vol. 20,
    no. 3, e1011941, Public Library of Science, 2024, doi:<a href="https://doi.org/10.1371/journal.pcbi.1011941">10.1371/journal.pcbi.1011941</a>.
  short: C. Chintaluri, M. Bejtka, W. Sredniawa, M. Czerwinski, J.M. Dzik, J. Jedrzejewska-Szmek,
    D.K. Wojciki, PLoS Computational Biology 20 (2024).
corr_author: '1'
date_created: 2024-03-24T23:00:59Z
date_published: 2024-03-14T00:00:00Z
date_updated: 2025-09-04T13:08:54Z
day: '14'
ddc:
- '000'
- '570'
department:
- _id: TiVo
doi: 10.1371/journal.pcbi.1011941
external_id:
  isi:
  - '001190689800001'
  pmid:
  - '38484020'
file:
- access_level: open_access
  checksum: c09718d0d09614642d877d0716ce32e8
  content_type: application/pdf
  creator: dernst
  date_created: 2025-06-25T05:47:36Z
  date_updated: 2025-06-25T05:47:36Z
  file_id: '19897'
  file_name: 2024_PLoSCompBio_Chintaluri.pdf
  file_size: 2540277
  relation: main_file
  success: 1
file_date_updated: 2025-06-25T05:47:36Z
has_accepted_license: '1'
intvolume: '        20'
isi: 1
issue: '3'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS Computational Biology
publication_identifier:
  eissn:
  - 1553-7358
  issn:
  - 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/Neuroinflab/kCSD-python
scopus_import: '1'
status: public
title: kCSD-python, reliable current source density estimation with quality control
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 20
year: '2024'
...