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Efficient spike detection and sorting for dense MEA

Project description

Herding Spikes 2

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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:

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.

Supported systems

Contributors, alphabetical

Quick start

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 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 numpy and 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:

3. Obtaining and installing HS2

Getting the code

Either download and uncompress:

Or install git from Then open a command prompt and type

git clone

This will create a folder HS2 in the current directory. Note that updates can now be simply retrieved by typing git pull.


To install, go to the HS2 directory, e.g.

cd HS2

and type

python install

Now HS2 will be available in the current virtual environment.

Example code

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 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.

Go here for documentation. A worked example for Biocam data is here.


The herders are based at the School of Informatics, University of Edinburgh. Contact us here, we are happy to help.

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