kCSD-python, reliable current source density estimation with quality control

Abstract

Interpretation of the extracellular recordings can be difficult due to the long range of electric field but can be facilitated by estimating the density of current sources (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method, and introduce several new techniques to facilitate CSD analysis of experimental data and interpretation of the results. We investigate the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for 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 to facilitate uptake by the community.