Tools for the Voytek Lab and friends to analyze neural time series.
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Project Status: Active – The project has reached a stable, usable state and is being actively developed. Latest Version Build Status License Supported Python Versions Binder

A package of modules to process and analyze neural recordings as individual voltage time series. The primary purpose of this library is to serve as the shared codebase for the Voytek Lab, but we welcome anyone's use and contributions.

Python version support

This package has been tested on python 3.4, 3.5, and 3.6 with the latest Anaconda distribution. Support for python 2 and earlier versions of python 3 is not guaranteed.


To install the latest release of neurodsp, you can install from pip:

$ pip install neurodsp

To get the development version (updates that are not yet published to pip), you can clone this repo.

$ git clone

To install this cloned copy of neurodsp, move into the directory you just cloned, and run:

$ pip install .


  • burst : Detect bursting oscillators in neural signals (Tutorial)
  • filt : Filter data with bandpass, highpass, lowpass, or notch filters (Tutorial)
  • laggedcoherence : Estimate rhythmicity using the lagged coherence measure (Tutorial)
  • sim : Simulate bursting or stationary oscillators with brown noise (Tutorial)
  • spectral : Compute spectral domain features (PSD and 1/f slope, etc) (Tutorial)
  • swm : Identify recurrent patterns in a signal using sliding window matching (Tutorial)
  • timefrequency : Estimate instantaneous measures of oscillatory activity (Tutorial)


  • numpy
  • scipy
  • matplotlib
  • scikit-learn
  • pytest (optional)