ViSAPy

ViSAPy (Virtual Spiking Activity in Python) is a tool for generation of biophysically realistic benchmark data for evaluation of spike sorting algorithms.

The tool is accompanied by the scientific publication:

Espen Hagen, Torbjørn V. Ness, Amir Khosrowshahi, Christina Sørensen, Marianne Fyhn, Torkel Hafting, Felix Franke and Gaute T. Einevoll. "ViSAPy: A Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms", Journal of Neuroscience Methods, Available online 4 February 2015, ISSN 0165-0270, http://dx.doi.org/10.1016/j.jneumeth.2015.01.029. (http://www.sciencedirect.com/science/article/pii/S0165027015000369)

ViSAPy was developed in the Computational Neuroscience Group (http://compneuro.umb.no), Department of Mathemathical Sciences and Technology (http://www.nmbu.no/imt) at the Norwegian University of Life Sciences (http://www.nmbu.no) and Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA, Jülich, Germany (http://www.fz-juelich.de/inm/inm-6/EN).

This work was supported by the Research Council of Norway (eVita, NOTUR, NevroNor), the International Neuroinformatics Coordinating Facility (INCF) through the Norwegian and German Nodes, the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 ("Human Brain Project"), the Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB), the European Community through the ERC Advanced Grant 267351 "NeuroCMOS", and the National Institutes of Health (NIH) through NIH grant R01EY019965.

This scientific software is released under the GNU Public License (see LICENSE file).

Dependencies

ViSAPy is a module implemented using Python (http://www.python.org) and facilitates on other packages and modules developed for Python by an active open source community. To get started with (scientific) Python, a nice resource would be for example these pages: https://scipy-lectures.github.io.

To make ViSAPy work and depending on what sort of Python distribution is used, the following packages may have to be installed:

• numpy (http://www.numpy.org)
• scipy (http://www.scipy.org)
• matplotlib (http://matplotlib.org)
• mpi4py (http://mpi4py.scipy.org)
• h5py (http://www.h5py.org)
• Cython (http://cython.org)

Note that pre-built Python distributions such as Anaconda (https://store.continuum.io/cshop/anaconda/) or Enthought Canopy (https://www.enthought.com/products/canopy/) may come with such packages preinstalled. If not, depending on operating system, such packages can usually be installed easily using the operating system's or other package managers (apt-get, synaptic, macports) or using the easy_install or pip command line tools: ::

pip install <package name> --user

or: ::

sudo pip install <package name>

Further, ViSAPy plotting and example scripts may require the Python SpikeSort package (http://spikesort.org). The NEST (http://www.nest-initiative.org) and NEURON (http://neuron.yale.edu) simulation softwares must also be built with Python bindings (please refer to their respective installation instructions, but see below).

Finally ViSAPy is built around LFPy (http://compneuro.umb.no/LFPy) for computing extracellular potentials around multicompartment neuron models and uses NEURON internally. Detailed information on getting LFPy (and also setting up NEURON) is given on the page http://compneuro.umb.no/LFPy/information.html

ViSAPy has been developed and tested on OSX and Linux platforms running Python version 2.7.x. As NEST only do not support Windows, ViSAPy will only run on Posix based platforms (OSX, Linux, Unix).

Installation

After making sure that all prerequisites above are met, download the ViSAPy source codes from GitHub using the terminal. Make sure that git version control software is installed (http://git-scm.com): ::

cd /where/to/put/files
git clone https://github.com/espenhgn/ViSAPy.git ViSAPy

At this point it is now possible to install ViSAPy as any other Python package locally in the user's Python environment issuing the commands: ::

cd ViSAPy
python setup.py install --user

However, as the user may want to modify or contribute to ViSAPy one can build extension modules inplace and link the /path/to/ViSAPy to the PYTHONPATH environment variable: ::

cd ViSAPy
python setup.py build_ext -i

Then, on linux, unix or OSX operating systems edit the .bashrc or .profile file located in your home folder to include the line: ::

export PYTHONPATH=/path/to/ViSAPy/:$PYTHONPATH

Documentation

To generate the html documentation issue from the ViSAPy source code directory, it is possible to do so using Sphinx (http://sphinx-doc.org) with the command: ::

cd /path/to/ViSAPy
sphinx-build -b html docs path/to/dest

The main html file with the autogenerated documentation of ViSAPy should now be found in the location: ::

path/to/dest/index.html

Examples

As example files for ViSAPy we have provided a host of scripts reproducing results of our publication (http://dx.doi.org/10.1016/j.jneumeth.2015.01.029.). The example files are found in: ::

/path/to/ViSAPy/examples

As an overview of the different example files:

• examples/ISI_waveforms/ISI_waveforms.py corresponds to Results section 3.1.2 and figure references therein
• examples/example_figure_02.py corresponds to Figure 2 in the publication
• examples/example_in_vivo_tetrode.py corresponds to Results section 3.2 and figure references therein
• examples/example_in_vivo_tetrode.py corresponds to Results section 3.3 and figure references therein
• examples/example_in_vitro_MEA/example_in_vitro_MEA.py corresponds to Results section 3.4 and figure references therein