SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns.

The Python 3 module spikeship which implements the fast, unsupervised dissimilarity measure described in Sotomayor-Gómez, B., L., Battaglia, F. and Vinck, M. (2023). "SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns". Article, PLoS CB.

Setup

The dependencies can be installed by running ./env_setup.sh <ENV_NAME> with the optional argument specifying the target environment (which must be source-able). To setup the module, run python setup.py install. A jupyter notebook is available in notebooks/, along with a demo dataset, showing an example workflow for the SpikeShip methods and its comparison with SPOTDis.

Linux (Anaconda)

1. conda create -n spikeship python=3
2. ./env_setup.sh spikeship
3. source activate spikeship
4. python setup.py install

Windows (Anaconda)

1. conda create -n spikeship python=3.6.5
2. conda activate spikeship
3. conda install numba
4. conda install ipykernel
5. conda install matplotlib
6. conda install scikit-learn
7. conda install joblib
8. python setup.py install

Further notes

The software requirements/dependencies are the same from the work , the implementation of the Spike Pattern Optimal Transport Dissimilarity described in Grossberger, L., Battaglia, F. and Vinck, M. (2018). Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. PLoS CB.