neo
Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats
Decision gist · record as of 2026-08-14
Yes. Neo is actively maintained, has no known security vulnerabilities, uses a permissive license, and is the de facto standard data model for electrophysiology in Python. Install it if you work with neural recordings, need to read multiple file formats, or want to integrate with other neuroscience tools that depend on Neo's object model.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; numpy and quantities packages must be installed as runtime dependencies.
- Installation is straightforward with low friction; the package has three runtime dependencies (packaging, numpy, quantities) and is actively maintained with a recent release within the last 50 days.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive, allowing commercial use, modification, and distribution with minimal restrictions beyond retaining the license notice.
last release 2026-06-25 (50 days) · last repo commit 2026-08-11 · 362 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 126,420 downloads/mo, #11,776 on PyPI
Alternatives
Verify before relying
pip install neo
import neo
block = neo.Block(name="recording")
segment = neo.Segment(name="trial_1")
block.segments.append(segment)- Whether the package supports reading and writing all listed formats equally well or if some are read-only.
- Performance characteristics when working with large multi-electrode datasets.
- Whether unit conversion and dimensional consistency checks have measurable overhead.
What it is and what it does
Neo is a data representation layer for electrophysiology research, built on top of NumPy and the quantities package to add physical units and dimensional consistency to neural recording data. It defines a hierarchical object model (Block, Segment, ChannelIndex, Unit, etc.) that maps naturally to intracellular, extracellular, and multi-electrode recordings, and provides I/O support for both proprietary formats (Spike2, Plexon, Blackrock, Tdt, AlphaOmega, NeuroExplorer, Axon) and open formats like HDF5.
Neo is deliberately designed as a lightweight data container with no analysis or visualization functions—its role is to standardize how electrophysiology data is represented so that other tools (like Elephant for analysis, SpykeViewer for visualization, or PyNN for simulations) can work with a common object model. Data stored in Neo objects behave like NumPy arrays but carry metadata, enforce unit compatibility, and support automatic conversion between measurement scales.
Use it for
- Load multi-format neurophysiology recordings into a single standardized Python object for downstream analysis pipelines.
- Convert between proprietary spike recording formats (Spike2, Plexon, Blackrock) to enable cross-platform data sharing.
- Build analysis workflows that accept Neo data objects from multiple recording systems without format-specific parsing code.
- Store electrophysiology data with physical units and dimensional metadata to prevent unit conversion errors in calculations.
- Integrate electrophysiology data with simulation frameworks like PyNN that expect Neo-compatible data structures.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Neo is actively maintained, has no known security vulnerabilities, uses a permissive license, and is the de facto standard data model for electrophysiology in Python. Install it if you work with neural recordings, need to read multiple file formats, or want to integrate with other neuroscience tools that depend on Neo's object model.
Install
neo on PyPI
Before you install
Installation is straightforward with low friction; the package has three runtime dependencies (packaging, numpy, quantities) and is actively maintained with a recent release within the last 50 days.
Requires Python 3.10 or later; numpy and quantities packages must be installed as runtime dependencies.
License in practice
BSD-3-Clause is permissive, allowing commercial use, modification, and distribution with minimal restrictions beyond retaining the license notice.
Quickstart
pip install neo
import neo
block = neo.Block(name="recording")
segment = neo.Segment(name="trial_1")
block.segments.append(segment)Verify before relying
- Whether the package supports reading and writing all listed formats equally well or if some are read-only.
- Performance characteristics when working with large multi-electrode datasets.
- Whether unit conversion and dimensional consistency checks have measurable overhead.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagespackagingnumpyquantities |
| Maintenance | Actively maintained 50 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 126,420 / month, #11,776 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering |
Evidence: neo-0.14.5-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “electrophysiology data format”
- neoNeo provides a unified data model for representing electrophysiology…
- mne-bidsMNE-BIDS reads and writes neuroimaging datasets in BIDS format,…
- pynwbPyNWB provides a high-level Python API for reading, writing, and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also mne-bids · pynwb · mne · nibabel · nilearn · pyEDFlib · edfio · pybids · fiftyone-brain · nipype