koerding / spykes

Tools for spike data analysis and visualization

spykes

Almost any electrophysiology study of neural spiking data relies on a battery of standard analyses. Raster plots and peri-stimulus time histograms aligned to stimuli and behavior provide a snapshot visual description of neural activity. Similarly, tuning curves are the most standard way to characterize how neurons encode stimuli or behavioral preferences. With increasing popularity of population recordings, maximum-likelihood decoders based on tuning models are becoming part of this standard.

Yet, virtually every lab relies on a set of in-house analysis scripts to go from raw data to summaries. We want to improve this status quo in order to enable easier sharing, better reproducibility and fewer bugs.

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

At present, spykes comes with three classes:

• NeuroVis helps you plot beautiful spike rasters and peri-stimulus time histograms (PSTHs).
• PopVis helps you plot population summaries of PSTHs as normalized averages or heat maps.
• NeuroPop helps you estimate tuning curves of neural populations and decode stimuli from population vectors with maximum-likelihood decoding.

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.

Installation

Clone the repository.

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

Install spykes using pip as follows

$ cd spykes
$ pip install -e ./

How to use NeuroVis?

See:

• crcns_dataset_example.ipynb
• reaching_dataset_example.ipynb

How to use PopVis?

See:

• popvis_example.ipynb

How to use NeuroPop?

See:

• neuropop_simul_example.ipynb
• reaching_dataset_example.ipynb

Dependencies

Already distributed with Anaconda and Canopy.

• NumPy >= 1.6.1
• SciPy >= 0.14
• Matplotlib >= 1.5

We also use Numba to optimize certain functions. We recommend the latest stable version (>= 0.26.0).

$ pip install numba

Datasets

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.

Authors

• Pavan Ramkumar
• Hugo Fernandes

Acknowledgments

• Konrad Kording for funding and support