klusta 3.0.10

Spike detection and automatic clustering for spike sorting

# Klusta: automatic spike sorting up to 64 channels

[](https://travis-ci.org/kwikteam/klusta) [](http://codecov.io/github/kwikteam/klusta?branch=master) [](http://klusta.readthedocs.org/en/latest/) [](https://pypi.python.org/pypi/klusta) [](https://github.com/kwikteam/klusta/releases/latest)

[klusta](https://github.com/kwikteam/klusta) is an open source package for automatic spike sorting of multielectrode neurophysiological recordings made with probes containing up to a few dozens of sites.

We are also working actively on more sophisticated algorithms that will scale to hundreds/thousands of channels. This work is being done within the [phy project](https://github.com/kwikteam/phy), which is still experimental at this point.

## Overview

klusta implements the following features:

• Kwik: An HDF5-based file format that stores the results of a spike sorting session.

• Spike detection (also known as SpikeDetekt): an algorithm designed for probes containing tens of channels, based on a flood-fill algorithm in the adjacency graph formed by the recording sites in the probe.

• Automatic clustering (also known as Masked KlustaKwik): an automatic clustering algorithm designed for high-dimensional structured datasets.

## GUI

You will need a GUI to visualize the spike sorting results.

We have developed two GUI programs with the same features:

• KlustaViewa: older project, but widely used. This will be automatically installed if you follow the installation instructions below.

• phy KwikGUI: newer project, scales to hundreds/thousands of channels, still experimental. You’ll need to install [phy](https://github.com/kwikteam/phy) and [phy-contrib](https://github.com/kwikteam/phy-contrib) if you want to try the development version.

Both GUIs work with the same Kwik format.

## Technical details

klusta is written in pure Python. The clustering code, written in Python and Cython, currently lives in [another repository](https://github.com/kwikteam/klustakwik2/).

## Quick install guide

Note: the installation instructions will be simplified soon.

The following instructions will install both klusta and the KlustaViewa GUI.

1. Make sure that you have [miniconda](http://conda.pydata.org/miniconda.html) installed. You can choose the Python 3.5 64-bit version for your operating system (Linux, Windows, or OS X).

2. Download the environment file:

   • [Linux](https://raw.githubusercontent.com/kwikteam/klusta/master/installer/environment-linux.yml)

   • [OS X](https://raw.githubusercontent.com/kwikteam/klusta/master/installer/environment-osx.yml)

   • [Windows](https://raw.githubusercontent.com/kwikteam/klusta/master/installer/environment-win.yml)

3. Open a terminal (on Windows, cmd, not Powershell) in the directory where you saved the file and type:

   `bash conda install conda=3 --yes conda env create -n klusta -f environment-XXX.yml # replace `XXX` by your system source activate klusta # omit the `source` on Windows conda install numpy=1.8 --yes `

4. Done! Now, to use klusta and KlustaViewa, you have to first type source activate klusta in a terminal (omit the source on Windows), and then call klusta or klustaviewa. See the documentation for more details.

### Updating the software

To get the latest version of the software, open a terminal and type:

` source activate klusta # omit the `source` on Windows pip install klusta klustaviewa kwiklib --upgrade `

## Links

• [Documentation](http://klusta.readthedocs.org/en/latest/) (work in progress)

• [Paper in Nature Neuroscience (April 2016)](http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.4268.html)

• [Mailing list](https://groups.google.com/forum/#!forum/klustaviewas)

• [Sample data repository](http://phy.cortexlab.net/data/) (work in progress)

## Credits

klusta is developed by [Cyrille Rossant](http://cyrille.rossant.net), [Shabnam Kadir](https://iris.ucl.ac.uk/iris/browse/profile?upi=SKADI56), [Dan Goodman](http://thesamovar.net/), [Max Hunter](https://iris.ucl.ac.uk/iris/browse/profile?upi=MLDHU99), and [Kenneth Harris](https://iris.ucl.ac.uk/iris/browse/profile?upi=KDHAR02), in the [Cortexlab](https://www.ucl.ac.uk/cortexlab), University College London.