Semi-Supervised Spike Sorting

This Python repository is adapted from the methods and code described in Mukherjee, Wachutka, & Katz (2017) [1]. A large percentage of the clustering parameters were made in reference to Reddish (2005) [2].

Note

This program is designed for sorting spikes from electrophysiological recordings into single, isolated units. The primary input is a .h5 file containing the continuous signal or thresholded waveforms.

Compatibility and Licensing

This software is compatible with Windows, macOS, and Linux, and is well-suited for containerization and high-performance computing clusters. It is distributed under the GNU General Public License v3.0 (GPLv3). For more information, consult the LICENSE file in this repository.

Usage

Analysis

The primary folder used for analysis is the ‘superplots’ folder. The ‘Plots’ folder contains individual plots, but in ‘superplots’, they are compiled for user convenience.

Note

Important files include .info, which contains information about the sort run, and clustering_results_compiled_isoi.xlsx, which contains details about each cluster.

Criteria

The primary criteria for considering a unit isolated are:

1. 1 ms ISIs must be <= 0.5%

2. The waveform must be cellular

3. The unit must be sufficiently separated based on Mahalanobis distribution

4. L-Ratio must be <= 0.1, as described in Schmitzer-Torbert et al. (2005) [2].

Note

For structuring the plot paths and further details on L-Ratio, refer to the Autosort configuration file.

Post-Processing

The post-processing is carried out via a GUI.

Note

This step requires both the .h5 files and the output folders from the Processing step.

Pipeline

The pipeline functions as follows:

1. Pre-Processing: Data is extracted from Spike2 files and packaged into .h5 files.

2. Processing: Multiple steps are performed, as detailed in Mukherjee et al. (2017) [1].

3. Post-Processing: The data is packaged into .json files.

References

[1] (1,2)

Mukherjee, Narendra & Wachutka, Joseph & Katz, Donald. (2017). Python meets systems neuroscience: affordable, scalable and open-source electrophysiology in awake, behaving rodents. 98-105.

[2] (1,2)

Schmitzer-Torbert N, Jackson J, Henze D, Harris K, Redish AD. Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience. 2005;131:1–11.

External Resources

For interacting with Spike2 data, the SonPy library is used and available via pypi.org.