All Tools

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.

The Neo Viewer Service is a Django app that provides a REST API for reading electrophysiology data from any file format supported by Neo and exposing it in JSON format.

NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems, rather than on the exact morphology of individual neurons. It is ideal for networks of any size, including models of information processing (e.g. in the visual or auditory cortex of mammals), models of network activity dynamics (e.g. laminar cortical networks or balanced random networks) and models of learning and plasticity. NEST is openly available for download.

NEST Desktop is a web-based GUI application for NEST Simulator, an advanced simulation tool for computational neuroscience. NEST Desktop enables the rapid construction, parametrization, and instrumentation of neuronal network models. It offers interactive tools for visual network construction, running simulations in NEST and applying visualization to support the analysis of simulation results. NEST Desktop mainly consists of two views and a connection to a server-based NEST instance, which can be controlled using the web-based NEST Desktop front-end. The first view of NEST Desktop enables the user to create point neuron network models interactively. A visual modeling language is provided and a simulation script is automatically created from this visual model. The second view enables the user to analyze the returned simulation results using various visualization methods. NEST Desktop offers additional functionality, such as employing Elephant for more sophisticated statistical analyses.

The NEST Instrumentation App is a graphical interface to connect recording and stimulation devices to networks. The interface is easy to use, is versatile, and gives a good visualization of the connection between devices and neurons. After having selected the desired connections, the projections can be sent to NEST to run simulations.

NESTML is a domain-specific language that supports the specification of neuron models in a precise and concise syntax. It was developed to address the maintainability issues that follow from an increasing number of models, model variants, and an increased model complexity in computational neuroscience. Our aim is to ease the modelling process for neuroscientists both with and without prior training in computer science. This is achieved without compromising on performance by automatic source-code generation, allowing the same model file to target different hardware or software platforms by changing only a command-line parameter. While originally developed in the context of NEST Simulator, the language itself as well as the associated toolchain are lightweight, modular and extensible, by virtue of using a parser generator and internal abstract syntax tree (AST) representation, which can be operated on using well-known patterns such as visitors and rewriting. Model equations can either be given as a simple string of mathematical notation or as an algorithm written in the built-in procedural language. The equations are analyzed by the associated toolchain ODE-toolbox, to compute an exact solution if possible or to invoke an appropriate numeric solver otherwise.

NetPyNE provides programmatic and graphical interfaces to develop data-driven multiscale brain neural circuit models using Python and NEURON. Users can define models using a standardised JSON-compatible, rule-based, declarative format Based on these specifications, NetPyNE will generate the network in NEURON, enabling users to run parallel simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis (e.g.,generate connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures). NetPyNE also facilitates model sharing by exporting and importing standardized formats: NeuroML and SONATA.

The UI splits the workflows in two tabs available at the top of the screen: define your network and create network. The NetPyNE GUI is implemented on top of Geppetto, an open-source platform that provides the infrastructure for building tools for visualizing neuroscience models and data and for managing simulations in a highly accessible way. The GUI is defined using JavaScript, React and HTML5. This offers a flexible and intuitive way to create advanced layouts while still enabling each of the elements of the interface to be synchronized with the Python model. The interactive Python backend is implemented as a Jupyter Notebook extension which provides direct communication with the Python kernel. This makes it possible to synchronize the data model underlying the GUI with a custom Python-based NetPyNE model. This functionality is at the heart of the GUI and means any change made to the NetPyNE model in the Python kernel is immediately reflected in the GUI and vice versa. The tool’s GUI is available at https://github.com/Neurosim-lab/NetPyNE-UI and is under active development.

The NetworkUnit module builds upon the formalized validation scheme of the SciUnit package, which enables the validation of models against experimental data (or other models) via tests. A test is matched to the model by capabilities and quantitatively evaluated by a score.

Neurolucida is a microscopy system specifically designed for performing accurate neuron reconstructions directly from histological specimens. It is capable of over 500 quantitative morphometric analyses, including: The number of dendrites, axons, branches, synapses, varicosities, and spines The length, width, and volume of dendrites and axons The area and volume of somas The complexity and extension of neurons A complete Neurolucida system includes all necessary hardware—including a microscope, computer, motorized XY stage, and camera—as well as technical and research support from MBF Bioscience to optimize your experimental design and analysis. The Neurolucida software and hardware work in harmony to deliver a powerful, easy to use neuron reconstruction system.

NeuroM is a Python toolkit for the analysis and processing of neuron morphologies It includes functionality to analyze (features like radial distances, volumes, neurite type counts, sholl anaylsis, etc.), visualize, and check neuron morphologies (disconnected neurites, duplicated points, zero diameters, etc). It can be used as a library, but also includes a command line interface to perform more common operations.

The Neuromorphic Computing Platform allows neuroscientists and engineers to perform experiments with configurable neuromorphic computing systems. The platform provides two complementary, large-scale neuromorphic systems built in custom hardware at locations in Heidelberg, Germany (the “BrainScaleS” system, also known as the “physical model” or PM system) and Manchester, United Kingdom (the “SpiNNaker” system, also known as the “many core” or MC system). Both systems enable energy-efficient, large-scale neuronal network simulations with simplified spiking neuron models. The BrainScaleS system is based on physical (analogue) emulations of neuron models and offers highly accelerated operation (104 x real time). The SpiNNaker system is based on a digital many-core architecture and provides real-time operation. The Python client allows scripted access to the Platform. The same client software is used both by end users for submitting jobs to the queue, and by the hardware systems to take jobs off the queue and to post the results.

NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells.

This tool allows for neuronal soma segmentation in fluorescence microscopy imaging datasets with the use of a parametrized family of deeplearning-based models based on the original U-Net model by Ronneberger et al. with some additional features such as residual links and tile-based frame reconstruction.

This tool presents a new technique for the generation of three-dimensional models for neuronal cells from the morphological information extracted through computed-aided tracing applications. The 3D polygonal meshes that approximate the cell membrane can be generated at different resolution levels, allowing balance to be reached between the complexity and the quality of the final model. Neuronize implements a novel approach to generate a realistic 3D shape of the soma from the incomplete information stored in the digitally traced neuron using a physical deformation technique. The addition of a set of spines along the dendrites completes the model, generating a final 3D neuronal cell suitable for its visualization in a wide range of 3D environments.

NeuroR is a collection of tools to repair morphologies. There are presently three types of repair which are outlined below. Sanitization This is the process of sanitizing a morphological file. It currently: ensures it can be loaded with MorphIO raises if the morphology has no soma or of invalid format removes unifurcations set negative diameters to zero raises if the morphology has a neurite whose type changes along the way removes segments with near zero lengths (shorter than 1e-4) Note: more functionality may be added in the future Cut plane repair The cut plane repair aims at regrowing part of a morphologies that have been cut out when the cell has been experimentally sliced. neuror cut-plane repair contains the collection of CLIs to perform this repair. Additionally, there are CLIs for the cut plane detection and writing detected cut planes to JSON files: If the cut plane is aligned with one of the X, Y or Z axes, the cut plane detection can be done automatically with the CLIs: neuror cut-plane file neuror cut-plane folder If the cut plane is not one the X, Y or Z axes, the detection has to be performed through the helper web application that can be launched with the following CLI: neuror cut-plane hint Unravelling Unravelling is the action of “stretching” the cell that has been shrunk because of the dehydratation caused by the slicing. The unravelling CLI sub-group is: neuror unravel The unravelling algorithm can be described as follows: Segments are unravelled iteratively. Each segment direction is replaced by the averaged direction in a sliding window around this segment. The original segment length is preserved. The start position of the new segment is the end of the latest unravelled segment.

NeuroScheme uses schematic representations, such as icons and glyphs to encode attributes of neural structures (i.e. neurons, columns, layers, populations, etc.). This abstraction alleviates problems with displaying, navigating, and analysing, large datasets. It has been designed specifically to manage hierarchically organised neural structures; one can navigate through the levels of the hierarchy, and hone in on their desired level of details. NeuroScheme works using what we call "domains". These domains specify which entities, attributes and relationships are going to be used for a specific use case. NeuroScheme currently has two built-in domains: “cortex” and “congen”. The “cortex” domain is designed for navigating and analysing cerebral cortex structures (i.e. neurons, micro-columns, columns, layers, etc.). The “congen” domain can be used to define the properties of both cells and connections, create circuits composed of neurons, and build populations. Groups of populations can be easily moved to a higher level of abstraction (such as column or layer), allowing one to create complex networks with little effort. These circuits can be exported afterwards and used for further analysis and simulations.

Neurosift is a browser-based tool designed for the visualization of NWB (Neurodata Without Borders) files, whether stored locally or hosted remotely, and enables interactive exploration of the DANDI Archive.

The C++ Neuroanatomy library provides analysis and editing functionalities for 3D traced neurons. Imports traced neurons written in SWC and 'Neurolucida' DAT and ASC format and validates them. A extensive set of predefined measures are included, but new measures can be added easily.

The goal of neurostr is, to provide an R interface to the NeuroSTR C++ neuroanatomy toolbox. The 'neurostr' package provides a subset of functionalities, via wrappers for the NeuroSTR precompiled tools: Node Feature Extractor; Branch Feature Extractor; Format converter; Neuron Validator.