kCSD-python

kCSD-python implements the Kernel Current Source Density (kCSD) method to estimate current source density from extracellular recordings and facilitate analysis and interpretation of experimental data.

Key Features:

• Arbitrary electrode distributions: kCSD-python allows CSD estimation for arbitrary distributions of electrodes in 1D, 2D, and 3D.
• Flexible source assumptions: The package supports assumptions of source distributions in tissue, a slice, or in a single cell.
• Diagnostic aids: kCSD-python includes a range of diagnostic aids to validate analysis results and counter limitations imposed by noise and assumptions in the method.
• Quality control: The package provides tools to address challenges in CSD estimation related to noise and method assumptions.

Scientific Applications:

• Extracellular recording interpretation: kCSD-python mitigates challenges in interpreting extracellular recordings caused by the long range of electric field by estimating current source density.
• CSD analysis workflow: The package supports typical analytical workflows for CSD analysis of experimental data, as demonstrated through its main functionalities.

Methodology:

The Kernel Current Source Density (kCSD) method is implemented to estimate current source density from extracellular recordings.