IBL Spike Sorting

This is the implementation of the IBL spike sorting pipeline described on this white paper: (https://doi.org/10.6084/m9.figshare.19705522.v4). The clustering part is based on the original MATLAB version of Kilosort 2.5, written by Marius Pachitariu.

Usage

We provide a few datasets to explore parametrization and test on several brain regions. The smallest dataset is a 100 seconds excerpt to test the installation. You can see the download instructions Here

1. if you want to override default parameters, copy the in the same folder as your raw binary file and edit its values
2. make sure you have a scratch directory with > 200 Gb of disk space to write temporary data
3. run the command iblsorter /path/to/raw_data_file.bin --scratch_directory /path/to/scratch

SCRATCH_DIR=~/scratch  # SSD drive with > 200Gb space to write temporary data
INPUT_FILE=/datadisk/Data/spike-sorting/integration-tests/stand-alone/imec_385_100s.ap.bin
iblsorter $INPUT_FILE --scratch_directory $SCRATCH_DIR

Additionally, iblsorter --help will display additional command options.

Installation

System Requirements

The code makes extensive use of the GPU via the CUDA framework. A high-end NVIDIA GPU with at least 8GB of memory is required. The solution has been deployed and tested on Cuda 12+ and Python 3.10, 3.11 and 3.12

Python environment

Only on Linux, first install fftw by running the following

sudo apt-get install -y libfftw3-dev

Navigate to the desired location for the repository and clone it

git clone https://github.com/int-brain-lab/ibl-sorter.git
cd ibl-sorter

Installation for cuda 11.x

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install cupy-cuda11x
pip install -e .

Installation for cuda 12.x (as of October 2024, check installation instructions from pytorch for the latest)

pip3 install torch torchvision torchaudio
pip install cupy-cuda12x
pip install -e .

Making sure the installation is successful and CUDA is available

Here we make sure that both cupy and torch are installed and that the CUDA framework is available.

from iblsorter.utils import cuda_installation_test
cuda_installation_test()

Then we can run the integration test.