Code & Data

Our lab codebase is almost entirely in Python. Below we break out our software into three domains: Scientific, Paper-specific, and Teaching & Educational.

While we host our code primarily on the lab's Github repo, several of our projects have expended to full-fledged packages hosted on their own independent project repos (listed below). This is done in the spirit of open source development and not having any one research group "own" a project.

For a friendly introduction to Git, check out our post here.

As you can see, we strongly believe in open science, open data, and collaboration over competition. Voytek has published a few Reviews/Perspectives on this topic:

• Voytek B, PLOS Computational Biology, The Virtuous Cycle of a Data Ecosystem
• Voytek B, Neuron, Social Media, Open Science, and Data Science Are Inextricably Linked

Scientific Software

We've got a number of Jupyter notebook Tutorials on the lab repo on there for basic field potential analysis methods. We also have done the initial development for three independent packages:

• NeuroDSP: neural digital signal processing
  • Citation: Cole S, Donoghue T, Gao R, Voytek B. NeuroDSP: A package for neural digital signal processing. J Open Source Softw, 2019.
  • repo
  • Sphinx documentation
• fooof: fitting oscillations and one-over-f (parameterization of neural power spectra)
  • Citation: Donoghue T*, Haller M*, Peterson E*, Varma P, Sebastian P, Gao R, Noto R, Knight RT, Shestyuk A¶, Voytek B¶. Parameterizing neural power spectra into periodic and aperiodic components. Nature Neuroscience, 2020.
  • repo
  • matlab wrapper
  • Sphinx documentation
• bycycle: cycle-by-cycle analysis of neural oscillations
  • Citation: Cole & Voytek. Cycle-by-cycle analysis of neural oscillations. J Neurophysiol, 2019.
  • repo
  • Sphinx documentation

Paper-specific Code

We also include code and data (where possible and permitted) for our manuscripts. The list of papers for which these are available are below:

• Gao R, van den Brink R, Pfeffer T, Voytek B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture (paper). eLife, 2020.
  • Code and data

• Gao R, Peterson EJ, Voytek B. Inferring synaptic excitation/inhibition balance from field potentials (PDF). NeuroImage, 2017.
  • Code and data

• Cole SR, van der Meij R, Peterson EJ, de Hemptinne C, Starr PA, Voytek B. Nonsinusoidal beta oscillations reflect cortical pathophysiology in Parkinson's disease (PDF). J Neurosci, 2017.
• • Code and data

Teaching & Educational Software

Lab members also teach a number of data science, programming, and neural analytics classes.

• COGS 9 - Introduction to Data Science: Developed by Voytek
  • Citation: forthcoming
  • repo

• COGS 18 - Introduction to Python: Developed by lab PhD student, Tom Donoghue
  • Citation: forthcoming
  • repo
  • Sphinx documentation

• COGS 108 - Data Science in Practice: Developed by Voytek and lab PhD student, Tom Donoghue
  • Citation: Donoghue, Voytek, Ellis. Teaching Creative and Practical Data Science at Scale. J Stats Education, 2020.
  • repo
  • Sphinx documentation

• COGS 138 - Neural Data Science: Developed by Voytek and Ashley Juavinett
  • Citation: forthcoming
  • repo
  • Sphinx documentation