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Python module that offers functions for measuring the similarity between two segmented multi-neuronal spiking activities.

Project description

# <img src=”docs/spykesim_logo/wtext/spykesim_wtext.svg” width=”320px”> [![PyPI](https://img.shields.io/pypi/v/spykesim.svg)](https://pypi.org/project/spykesim/) [![MIT License](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](LICENSE) [![Build Status](https://travis-ci.org/KeitaW/spykesim.svg?branch=master)](https://travis-ci.org/KeitaW/spykesim)

spykesim is a Python module that offers functions for measuring the similarity between two segmented multi-neuronal spiking activities.

Extended edit similarity measurement is implemented. You can find details in the following paper.

https://www.frontiersin.org/articles/10.3389/fninf.2019.00039

This library is re-implementation of the algorithm. The original implementation can be found in [this repo](https://github.com/KeitaW/Chaldea).

# Supported Operating Systems This library tested on Ubuntu and MacOS.

For Windows users: Please consider to use Ubuntu via [Windows Subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/install-win10).

# Installation If you do not have Python3.7 on your environment, you may use [Anaconda](https://www.anaconda.com/distribution/).

[Cython](https://github.com/cython/cython) and [Numpy](https://github.com/numpy/numpy) needs to be preinstalled as these will be used in the installation process.

If you have not installed these packages, run the following: `bash pip install numpy cython ` You can install this library via pip as well: `bash pip install spykesim ` or you may clone and build by yourself: `bash git clone https://github.com/KeitaW/spykesim.git cd spykesim python setup.py build_ext --inplace install `

## Dependencies

  • Python (>= 3.7)
  • Numpy(Needs to be preinstalled)
  • Cython(Needs to be preinstalled)
  • scipy
  • tqdm
  • h5py

# Tutorial You can find a tutorial in [doc](https://github.com/KeitaW/spykesim/blob/master/docs/tutorial.ipynb).

# Citation You can use the following bib entry to cite this work: ` @article{Watanabe:2019eq, author = {Watanabe, Keita and Haga, Tatsuya and Tatsuno, Masami and Euston, David R and Fukai, Tomoki}, title = {{Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering}}, journal = {Frontiers in Neuroinformatics}, year = {2019}, volume = {13}, month = may } ` #

This project uses the following repository as a template.

https://github.com/kennethreitz/samplemod The original LICENSE file can be found in [here](misc/original_license.md).

Project details


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