Efficient spike detection and sorting for dense MEA
Herding Spikes 2
Software for high density electrophysiology
This software provides functionality for the detection, localisation and clustering of spike data from dense multielectrode arrays based on the methods described in the following papers:
J.-O. Muthmann, H. Amin, E. Sernagor, A. Maccione, D. Panas, L. Berdondini, U.S. Bhalla, M.H. Hennig MH (2015). Spike detection for large neural populations using high density multielectrode arrays. Front. Neuroinform. 9:28. doi: 10.3389/fninf.2015.00028.
G. Hilgen, M. Sorbaro, S. Pirmoradian, J.-O. Muthmann, I. Kepiro, S. Ullo, C. Juarez Ramirez, A. Puente Encinas, A. Maccione, L. Berdondini, V. Murino, D. Sona, F. Cella Zanacchi, E. Sernagor, M.H. Hennig (2016). Unsupervised spike sorting for large scale, high density multielectrode arrays. Cell Reports 18, 2521–2532. bioRxiv: http://dx.doi.org/10.1101/048645.
This implementation is highly efficient, spike detection and localisation runs in real time on recordings from 4,096 channels at 7kHz on a desktop PC. Large recordings with millions of events can be sorted in minutes.
Since we believe publicly funded research code should be free and open, all code is released under GPL-3.0.
- Matthias Hennig: Spike sorting
- Jano Horvath: Parameter optimisation
- Cole Hurwitz: Spike detection, localisation and sorting, C++ code
- Oliver Muthmann: Spike detection and localisation
- Albert Puente Encinas: C++ implementation, optimisation and parallelisation
- Martino Sorbaro: Spike sorting, class structure and much of the python code
- Cesar Juarez Ramirez: Visualisation
- Raimon Wintzer: GUI and visualisation
The code has been tested with Python version 3.6. It is essential
numpy is available before installing.
The other dependencies will be installed by the installer.
If your system does not have Python pre-installed, the Anaconda distribution may be used.
All operative systems - Installation via pip
We suggest you use Anaconda if you don't have a favourite Python installed yet. We also recommend installing the code in a virtual environment (see below in the "from source" sections).
A pip distribution is available and can be installed as follows:
pip install numpy scipy pip install herdingspikes
Note: HerdingSpikes is tested on Python 3.5 and above. It may work on Python 3.0-3.4.
To install from source, clone this repository and follow the instructions below.
Linux/Mac - from source
We suggest you install the code in a virtual environment. You can create one by running
python3 -m venv --system-site-packages desired/location/HS2venv source desired/location/HS2venv/bin/activate
You can omit
--system-site-packages if you don't want to use the local versions of common Python libraries. You will need to
activate whenever you're using the module.
The module can automatically be installed, including all dependencies, by running
pip install numpy scipy python setup.py install
Windows - from source
1. Creating a Python virtual environment
Option 1: GUI
Once Anaconda is installed, create and activate a virtual environment called
HS2env. This can be done with the
Anaconda Navigator per mouse click. To make sure
Cython are available, type
conda install -n C:\Users\HS2env numpy
Option 2: Command line (from source)
Alternatively, open the
Anaconda Prompt and type:
conda search "^python$"
This will display a list of available python versions. Here we choose 3.6.5:
conda create -n C:\Users\HS2env python=3.6.5 anaconda
This environment should be activated every time HS2 is used with the command
conda activate C:\Users\HS2env
To make sure
numpy is available, type
conda install -n C:\Users\HS2env numpy
2. Installing a C++ Compiler
HS2 contains fast C++ code, which requires a compiler. If you don't have a C++ compiler installed, the easiest solution is to download and install the Microsoft Visual Studio Build Tools: https://www.visualstudio.com/downloads/#build-tools-for-visual-studio-2017.
3. Obtaining and installing HS2
Getting the code
Either download and uncompress: https://github.com/mhhennig/HS2/archive/master.zip
git from https://git-scm.com/download/win. Then open a command prompt and type
git clone https://github.com/mhhennig/HS2.git
This will create a folder
HS2 in the current directory. Note that updates can now be simply retrieved by typing
To install, go to the HS2 directory, e.g.
python setup.py install
Now HS2 will be available in the current virtual environment.
Example code for the different supported systems is in the folder notebooks. These can be run without installing HS2 system-wide, but requires to run
python setup.py build_ext --inplace in the
HS2 directory. Next, run
jupyter notebook and navigate to the directory to try the code. Each notebook will download a short segment of raw data.
The herders are based at the School of Informatics, University of Edinburgh. Contact us here, we are happy to help.
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