Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, Igor Pro, and support for writing to a subset of these formats plus non-proprietary formats including Klustakwik and HDF5.

The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data, by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization.

Neo is used by a number of other software tools, including OpenElectrophy and SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing) and PyNN (simulations).

Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo’s data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion.

A project with similar aims but for neuroimaging file formats is NiBabel.


Neo is free software, distributed under a 3-clause Revised BSD licence (BSD-3-Clause).


If you have problems installing the software or questions about usage, documentation or anything else related to Neo, you can post to the NeuralEnsemble mailing list. If you find a bug, please create a ticket in our issue tracker.


Any feedback is gladly received and highly appreciated! Neo is a community project, and all contributions are welcomed - see the Developers’ guide for more information. Source code is on GitHub.