Tools for analyzing neural time series.
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Latest commit a8bb6f6 Feb 14, 2019


Project Status: Active – The project has reached a stable, usable state and is being actively developed. Latest Version Build Status codecov License Supported Python Versions Binder

A package of tools to process, analyze, and simulate neural recordings as individual voltage time series, with specific focus on time and frequency domain analyses.


NeuroDSP is written in Python, and requires Python >= 3.5 to run.

It has the following dependencies:

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

We recommend using the Anaconda distribution to manage these requirements.


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)