Installing Spike Sorters¶

An important aspect of spikeinterface is the spikeinterface.sorters module. This module wraps many popular spike sorting tools, allowing you to run multiple sorters on the same dataset with only a few lines of code and through Python.

Installing spike sorters can be painful! Many of them come with several requirements that could cause conflicts in your Python environment. To make things easier, we have created docker images for most of these sorters, and in many cases the easiest way to run them is to do so via docker or singularity. This is the approach we recommend for all users. To run containerized sorters see our documentation here: Running sorters in container docker/singularity.

There are some cases where users will need to install the spike sorting algorithms in their own environment. If you are on a system where it is infeasible to run docker or singularity containers, or if you are actively developing the spike sorting software, you will likely need to install each spike sorter yourself.

Some of theses sorters are written in Matlab, so you will also need to install Matlab if you want to use them (Kilosort, Kilosort2, Ironclust, …). Some of then will also need some computing libraries like CUDA (Kilosort, Kilosort2, Ironclust (optional)) or opencl (Tridesclous) to use hardware acceleration (GPU).

Here is a list of the implemented wrappers and some instructions to install them on your local machine. Installation instructions are given for an Ubuntu platform. Please check the documentation of the different spike sorters to retrieve installation instructions for other operating systems. We use pip to install packages, but conda should also work in many cases.

Some novel spike sorting algorithms are implemented directly in SpikeInterface using the spikeinterface.sortingcomponents module. Checkout the SpikeInterface-based spike sorters section of this page for more information!

If you experience installation problems please directly contact the authors of theses tools or write on the related mailing list, google group, etc.

Please feel free to enhance this document with more installation tips.

External sorters¶

Herdingspikes2¶

Python + C++
Url: https://github.com/mhhennig/hs2
Authors: Matthias Hennig, Jano Horvath,Cole Hurwitz, Oliver Muthmann, Albert Puente Encinas, Martino Sorbaro, Cesar Juarez Ramirez, Raimon Wintzer: GUI and visualisation
Installation:
```
pip install herdingspikes
```

HDSort¶

Matlab
Url: https://git.bsse.ethz.ch/hima_public/HDsort.git
Authors: Roland Diggelmann, Felix Franke

Installation:

git clone https://git.bsse.ethz.ch/hima_public/HDsort.git
# provide installation path by setting the HDSORT_PATH environment variable
# or using HDSortSorter.set_hdsort_path()

IronClust¶

Matlab
Url: https://github.com/jamesjun/ironclust
Authors: James J. Jun

Installation need Matlab:

git clone https://github.com/jamesjun/ironclust
# provide installation path by setting the IRONCLUST_PATH environment variable
# or using IronClustSorter.set_ironclust_path()

Kilosort¶

Matlab, requires CUDA
Url: https://github.com/cortex-lab/KiloSort
Authors: Marius Pachitariu

Installation needs Matlab and cudatoolkit:

git clone https://github.com/cortex-lab/KiloSort
# provide installation path by setting the KILOSORT_PATH environment variable
# or using KilosortSorter.set_kilosort_path()

See also for Matlab/cuda: https://www.mathworks.com/help/parallel-computing/gpu-support-by-release.html

Kilosort2¶

Matlab, requires CUDA
Url: https://github.com/MouseLand/Kilosort2
Authors: Marius Pachitariu

Installation needs Matlab and cudatoolkit:

git clone https://github.com/MouseLand/Kilosort2
# provide installation path by setting the KILOSORT2_PATH environment variable
# or using Kilosort2Sorter.set_kilosort2_path()

See also for Matlab/cuda: https://www.mathworks.com/help/parallel-computing/gpu-support-by-release.html

Kilosort2.5¶

Matlab, requires CUDA
Url: https://github.com/MouseLand/Kilosort
Authors: Marius Pachitariu

Installation needs Matlab and cudatoolkit:

git clone https://github.com/MouseLand/Kilosort
# provide installation path by setting the KILOSORT2_5_PATH environment variable
# or using Kilosort2_5Sorter.set_kilosort2_path()

See also for Matlab/cuda: https://www.mathworks.com/help/parallel-computing/gpu-support-by-release.html

Kilosort3¶

Matlab, requires CUDA
Url: https://github.com/MouseLand/Kilosort
Authors: Marius Pachitariu

Installation needs Matlab and cudatoolkit:

git clone https://github.com/MouseLand/Kilosort
# provide installation path by setting the KILOSORT3_PATH environment variable
# or using Kilosort3Sorter.set_kilosort3_path()

See also for Matlab/cuda: https://www.mathworks.com/help/parallel-computing/gpu-support-by-release.html

pyKilosort¶

Python, requires cuda
Url: https://github.com/int-brain-lab/pykilosort / https://github.com/MouseLand/pykilosort
Authors: Marius Pachitariu, Shashwat Sridhar, Alexander Morley, Cyrille Rossant, Kush Bunga
Install the python cuda toolkit. In principle, this should work:
```
pip install cupy  (or pip install cupy-cudaXXX)
```
However, conda installation could be less painful:
```
conda install cupy
```

Next, clone and install pykilosort. Note that we support the newer version on the develop branch and the ibl_prod version from the IBL fork:

pip install phylib, pypandoc
# recommended
git clone --branch ibl_prod https://github.com/int-brain-lab/pykilosort
# or
git clone --branch develop https://github.com/MouseLand/pykilosort
cd pykilosort
pip install -r requirements.txt
python setup.py install

Alternatively, you can use the pyks2.yml environment file in the pykilosort repo and update your favorite environment with:
```
conda env update --name my-fav-env --file pyks2.yml --prune
```
See also https://github.com/MouseLand/pykilosort#installation

Mountainsort4¶

Python
Url: https://github.com/flatironinstitute/mountainsort
Authors: Jeremy Magland, Alex Barnett, Jason Chung, Loren Frank, Leslie Greengard
Installation:
```
pip install mountainsort4
```

SpykingCircus¶

Python, requires MPICH
Url: https://spyking-circus.readthedocs.io
Authors: Pierre Yger, Olivier Marre

Installation:

sudo apt install libmpich-dev
pip install mpi4py
pip install spyking-circus --no-binary=mpi4py

Tridesclous¶

Python, runs faster with opencl installed but optional
Url: https://tridesclous.readthedocs.io
Authors: Samuel Garcia, Christophe Pouzat
Installation:
```
pip install tridesclous
```

Optional installation of opencl ICD and pyopencl for hardware acceleration:

sudo apt-get install beignet (optional if intel GPU)
sudo apt-get install nvidia-opencl-XXX (optional if nvidia GPU)
sudo apt-get install pocl-opencl-icd (optional for multi core CPU)
sudo apt-get install opencl-headers ocl-icd-opencl-dev libclc-dev ocl-icd-libopencl1
pip install pyopencl

Waveclus¶

Matlab
Also supports Snippets (waveform cutouts) objects (BaseSnippets)
Url: https://github.com/csn-le/wave_clus/wiki
Authors: Fernando Chaure, Hernan Rey and Rodrigo Quian Quiroga

Installation needs Matlab:

git clone https://github.com/csn-le/wave_clus/
# provide installation path by setting the WAVECLUS_PATH environment variable
# or using WaveClusSorter.set_waveclus_path()

Combinato¶

Python
Url: https://github.com/jniediek/combinato/wiki
Authors: Johannes Niediek, Jan Boström, Christian E. Elger, Florian Mormann

Installation:

git clone https://github.com/jniediek/combinato
# Then inside that folder, run:
python setup_options.py
# provide installation path by setting the COMBINATO_PATH environment variable
# or using CombinatoSorter.set_combinato_path()

Klusta (LEGACY)¶

Python
Requires SpikeInterface<0.96.0 (and Python 3.7)
Url: https://github.com/kwikteam/klusta
Authors: Cyrille Rossant, Shabnam Kadir, Dan Goodman, Max Hunter, Kenneth Harris

Installation:

pip install Cython h5py tqdm
pip install click klusta klustakwik2

See also: https://github.com/kwikteam/phy

Yass (LEGACY)¶

Python, cuda, torch
Requires SpikeInterface<0.96.0 (and Python 3.7)
Url: https://github.com/paninski-lab/yass
Authors: JinHyung Lee, Catalin Mitelut, Liam Paninski

Installation:

https://github.com/paninski-lab/yass/wiki/Installation-Local

SpikeInterface-based spike sorters¶

Thanks to the spikeinterface.sortingcomponents module, some spike sorting algorithms can now be fully implemented with SpikeInterface.

SpykingCircus2¶

This is a upgraded version of SpykingCircus, natively written in SpikeInterface. The main differences are located in the clustering (now using on-the-fly features and less prone to finding noise clusters), and in the template-matching procedure, which is now a fully orthogonal matching pursuit, working not only at peak times but at all times, recovering more spikes close to noise thresholds.

Python, requires HDBSCAN
Authors: Pierre Yger

Installation:

pip install hdbscan
pip install spikeinterface