ArticlePLoS computational biology2024
kCSD-python, reliable current source density estimation with quality control.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Induction of cortical on/off periods in awake mice fulfills sleep functions.Nature neuroscience · 2026Article
- A modular, high-bandwidth, bidirectional implantable device for neural interrogation.bioRxiv : the preprint server for biology · 2026Article
- A hippocampal 'sharp-wave sleep' state that is dissociable from cortical sleep.Nature neuroscience · 2026Article
- Induction of cortical ON/OFF periods in awake mice fulfills sleep functions.bioRxiv : the preprint server for biology · 2025Article
- Dynamic mapping of network-level LTP in the hippocampus via high-resolution bioelectrical sensing.APL bioengineering · 2025Article
- A Case Study on EEG Signal Correlation Towards Potential Epileptic Foci Triangulation.Sensors (Basel, Switzerland) · 2024Article
- NMDA receptor antagonist high-frequency oscillations are transmitted via bottom-up feedforward processing.Scientific reports · 2024Article
- Article
- Cross-population coupling of neural activity based on Gaussian process current source densities.PLoS computational biology · 2021Article
- Network and synaptic mechanisms underlying high frequency oscillations in the rat and cat olfactory bulb under ketamine-xylazine anesthesia.Scientific reports · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
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.
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