This 2.0 version of kCSD inverse method proposed in python3
Paper 1: "Kernel Current Source Density Method", J. Potworowski, W. Jakuczun, S. Łȩski, D. K. Wójcik; Neural Comput 2012; 24 (2): 541–575, doi: https://doi.org/10.1162/NECO_a_00236
Paper 2 : "What we can and what we cannot see with extracellular multielectrodes", C. Chintaluri, M. Bejtka, W. Średniawa, M. Czerwiński, J. M. Dzik, J. Jędrzejewska-Szmek, K. Kondrakiewicz, E. Kublik, D. K. Wójcik; PLoS Computational Biology (2021), 17(5): e1008615, doi: https://doi.org/10.1371/journal.pcbi.1008615
Paper 3 : "kCSD-python, reliable current source density estimation with quality control", C. Chintaluri, M. Bejtka, W. Średniawa, M. Czerwiński, J. M. Dzik, J. Jędrzejewska-Szmek, D. K. Wójcik; bioRxiv, doi: https://doi.org/10.1101/708511
Paper 1 is the original paper with software code in Matlab. Paper 2 is an improvement and development of the paper 1. Paper 3 is a feature showcase and walk-through of the method and its applications.
This library comes with three tutorials and does not require any installation.
More information on the tutorials is provided here Tutorials!
To have these tutorials and saved on your desktop, you will also need jupyter-notebook installed
pip install jupyterlabThis library includes all the necessary scripts to generate the figures for papers 2 and 3.
Autogenerated documentation available from readthedocs:
Also included here are authors and their contributions, citation policy, contacts etc.,
Please see git tags for earlier versions
- v1.2 corresponds to the first time kCSD-python released as a python package
- v1.0 corresponds to the version with the test cases written inside tests folder
- v1.1 corresponds to the elephant python library version - no tests here
Continuous Integration (Travis):
Test Coverage :
Documentation Status: