Spykes is a collection of Python tools to make the visualization and analysis of neural data easy and reproducible.

At present, this project comes with two packages:

• neurovis helps you plot beautiful spike rasters and peri-stimulus time histograms
• neuropop helps you estimate tuning curves of neural populations and decode stimuli from population vectors

Spykes deliberately does not aim to provide tools for spike sorting or file i/o with popular electrophysiology formats, but only aims to fill the missing niche for neural data analysis and easy visualization. For file i/o, see Neo and OpenElectrophy. For spike sorting, see Klusta.

Documentation, tutorials and examples are coming soon! Check out the notebooks for now.

Clone the repository.

$ git clone http://github.com/KordingLab/spykes

Install spykes using pip as follows

$ cd spykes
$ pip install -e ./

See:

• crcns_dataset_example.ipynb
• reaching_dataset_example.ipynb

See:

• neuropop_simul_example.ipynb
• reaching_dataset_example.ipynb

So far, you only need NumPy >= 1.6.1 and SciPy >= 0.14, which are already distributed with Anaconda and Canopy.

Some functions are optimized using Numba. We recommend the latest stable version (0.26.0 or later).

$ pip install numba

The example notebooks use two real datasets. Instructions for downloading these datasets are included in the notebooks. We recommend deepdish for reading the HDF5 datafile.

• Pavan Ramkumar
• Hugo Fernandes

• Konrad Kording for funding and support