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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, and support for writing
to a subset of these formats plus non-proprietary formats including 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 SpykeViewer (data analysis and visualization), Elephant (data analysis),
the G-node suite (databasing), PyNN (simulations), tridesclous (spike sorting) and ephyviewer (data visualization).
OpenElectrophy (data analysis and visualization) uses an older version of neo.

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.

Other software

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Dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex and their morphology appears to be critical from the functional point of view. Thus, characterizing this morphology is necessary to link structural and functional spine data and thus interpret and make them more meaningful. We have used a large database of more than 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons that is first transformed into a set of 54 quantitative features characterizing spine geometry mathematically. The resulting data set is grouped into spine clusters based on a probabilistic model with Gaussian finite mixtures. We uncover six groups of spines whose discriminative characteristics are identified with machine learning methods as a set of rules. The clustering model allows us to simulate accurate spines from human pyramidal neurons to suggest new hypotheses of the functional organization of these cells.

Data analysis and visualisationData

Android app for multimodal data acquisition from wearables

An app for acquiring and storing data from multiple sensors. Currently, can be used with the following devices: Empatica E4 Tablet/Smartphone built-in sensors MetaMotion R In order to improve reliability, a bipartite structure has been implemented. In particular, the Main Activity acts as an interface between the user and the main service that constitutes the principal actor. The latter performs scans, handles the user's requests to connect remote devices, all the unexpected disconnection that may happen and receives the data from the wireless sensors.


Bids Manager & Pipeline

Manually driven processes for data storing can lead to human errors, which cannot be tolerated in the context of a clinical data sets. The Bids manager offers a secure system to import and structure patient’s clinical data sets in Brain Imaging Data Structure (BIDS). BIDS is an initiative aiming at establishing a common standard to describe data and its organization on disk for both neuroimaging and electrophysiological data. Bids Manager is a software for clinicians and researchers with a user-friendly interface.

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