Tools

NESTML

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.

Modelling and simulationNetwork level simulation

NetPyNE

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.

Modelling and simulationCellular level simulation

NetPyNE GUI

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.

Modelling and simulationCellular level simulation

NetworkUnit

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.

Validation and inference

Neurolucida

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.

Modelling and simulation

NeuroM

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.

Modelling and simulation

NeuroM

Data analysis and visualisation

Neuromorphic Platform Python client

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.

Modelling and simulation

Neuron

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.

Modelling and simulationCellular level simulation

Neuronizev2

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.

Modelling and simulation

Neuron Segmentation Tool

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.

Modelling and simulation

NeuroR

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.

Modelling and simulation

NeuroR

Modelling and simulation

NeuroScheme

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.

Modelling and simulationCellular level simulationData analysis and visualisation

neurostr

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.

Modelling and simulation

NeuroSTR

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.

Modelling and simulation

NeuroSuites-BNs

NeuroSuites is an online platform to run multiple neuroscience tools in a very easy way. To start using NeuroSuites just click on your preferred category on the tab at the top of the page. It provides you multiple tools to analyze neuroscience data. You will not need to install anything to run the tools provided, everything is online. Furthermore, when you are done, you can export your results to your own computer. There are many available tools, some are focused in analyzing neurons morphology reconstructions and the others are general purpose tools like the statistics engine, supervised classification models, Bayesian networks, etc.

Data analysis and visualisation

NeuroTessMesh

Visualise neurons and neural circuits consisting of a large number of cells with NeuroTessMesh on your desktop. It enables the visualisation of the 3D morphology of cells included in open databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. NeuroTessMesh takes morphological tracings of cells acquired by neuroscientists as its only input. It generates 3D models that approximate the neuronal membrane. The resolution of the models can be adapted at the time of visualisation. NeuroTessMesh can assign different colours to different morphologies, in order to visually codify relevant morphological variables, or even neuronal activity.

Modelling and simulationCellular level simulationData analysis and visualisation

Results: 145 - 162 of 252

Make the most out of EBRAINS