IBL Spike Sorting

This is a Python port of the original MATLAB version of Kilosort 2.5, written by Marius Pachitariu.

TODO: changes The modifications are described in this presentation We are working on an updated version of the whitepaper, but in the meantime, you can refer to the previous version here.

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. Here is the minimal working example:

import shutil
from pathlib import Path

from iblsorter.ibl import run_spike_sorting_ibl, ibl_pykilosort_params, download_test_data

if __name__ == "__main__":
    data_path = Path("/mnt/s0/spike_sorting/integration_tests")  # path on which the raw data will be downloaded
    scratch_dir = Path.home().joinpath("scratch",
                                    'iblsorter')  # temporary path on which intermediate raw data will be written, we highly recommend a SSD drive
    ks_output_dir = Path("/mnt/s0/spike_sorting/outputs")  # path containing the kilosort output unprocessed
    alf_path = ks_output_dir.joinpath(
        'alf')  # this is the output standardized as per IBL standards (SI units, ALF convention)

    # download the integration test data from amazon s3 bucket
    bin_file, meta_file = download_test_data(data_path)

    # prepare and mop up folder architecture for consecutive runs
    DELETE = True  # delete the intermediate run products, if False they'll be copied over to the output directory for debugging
    shutil.rmtree(scratch_dir, ignore_errors=True)
    scratch_dir.mkdir(exist_ok=True)
    ks_output_dir.mkdir(parents=True, exist_ok=True)

    # loads parameters and run
    params = ibl_pykilosort_params(bin_file)
    params['Th'] = [6, 3]

    run_spike_sorting_ibl(bin_file, delete=DELETE, scratch_dir=scratch_dir,
                            ks_output_dir=ks_output_dir, alf_path=alf_path, log_level='INFO', params=params)

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+.

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

pip install cupy-cuda12x
pip3 install torch torchvision torchaudio
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()