# Getting Started¶

## What is 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 four 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.
• STRF helps you estimate spatiotemporal receptive fields.

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.

## Installing¶

For most cases (including following along with the examples) it is sufficient to just install the vanilla version.

### Vanilla¶

This installs the current version from PyPi.

pip install spykes


### Bleeding-Edge¶

pip install git+git://github.com/KordingLab/spykes


### Local Version¶

This creates a local copy of the repo, where you can make changes to Spykes that get propagated to your project.

git clone http://github.com/KordingLab/spykes  # Clone this somewhere useful
pip install -e .[develop]


## Datasets¶

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