{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Neo provides a unified data model for representing electrophysiology and EEG data in Python, with built-in support for reading and writing multiple neurophysiology file formats including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, and Tdt.","skillfed_tags":["neuroscience","electrophysiology","data-format"],"use_cases":["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."],"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.\n\nNeo is deliberately designed as a lightweight data container with no analysis or visualization functions\u2014its 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.","worth_installing":"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."},"id":"neo","links":{"html":"https://skillfed.io/packages/neo","md":"https://skillfed.io/packages/neo.md","pypi":"https://pypi.org/project/neo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-25","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"neo","python_support":"supports_current","summary":"Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats"},"popularity":{"monthly_downloads":126420,"position":11776,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.14.5"}