Tools

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

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

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

NMODL Framework

The NMODL Framework is a code generation engine for NEURON MODeling Language (NMODL). It is designed with modern compiler and code generation techniques to: Provide modular tools for parsing, analysing and transforming NMODLProvide easy to use, high level Python APIGenerate optimised code for modern compute architectures including CPUs, GPUs Flexibility to implement new simulator backendsSupport for full NMODL specification.

Modelling and simulation

ODE-toolbox

Choosing the optimal solver for systems of ordinary differential equations (ODEs) is a critical step in dynamical systems simulation. ODE-toolbox is a Python package that assists in solver benchmarking, and recommends solvers on the basis of a set of user-configurable heuristics. For all dynamical equations that admit an analytic solution, ODE-toolbox generates propagator matrices that allow the solution to be calculated at machine precision. For all others, first-order update expressions are returned based on the Jacobian matrix. In addition to continuous dynamics, discrete events can be used to model instantaneous changes in system state, such as a neuronal action potential. These can be generated by the system under test as well as applied as external stimuli, making ODE-toolbox particularly well-suited for applications in computational neuroscience.

Modelling and simulation

openMINDS metadata for TVB-ready data

This Jupyter notebook contains Python code for creating openMINDS JSON-LD metadata collections for TVB-ready data. The code in this notebook was used to generate openMINDS metadata for curation of TVB-on-EBRAINS datasets using openMINDS v1. An overview over TVB-on-EBRAINS services is provided in the preprint https://arxiv.org/abs/2102.05888 The openMINDS schema standard specification is hosted in the repository https://github.com/HumanBrainProject/openMINDS

Whole-brain simulationModelling and simulation

PIPSA

PIPSA (Protein Interaction Property Similarity Analysis) is a method to compare proteins according to their interaction properties. PIPSA may assist in function assignment, and the estimation of binding properties and enzyme kinetic parameters. The PIPSA webserver, webPIPSA, computes protein electrostatic potentials and corresponding similarity indices for a user-defined set of proteins. The standalone code, multipipsa, which includes a python wrapper, provides further options, including running PIPSA on multiple sites on a protein and performing a comparative analysis of the binding properties of user-defined groups of proteins.

Modelling and simulationMolecular and subcellular simulation

PyCOMPSs

PyCOMPSs is the Python binding of COMPSs, a programming model and runtime which aims to ease the development of parallel applications for distributed infrastructures, such as Clusters and Clouds. The Programming model offers a sequential interface but at execution time the runtime system is able to exploit the inherent parallelism of applications at task level. The framework is complemented by a set of tools for facilitating the development, execution monitoring and post-mortem performance analysis. A PyCOMPSs application is composed of tasks, which are methods annotated with decorators following the PyCOMPSs syntax. At execution time, the runtime builds a task graph that takes into account the data dependencies between tasks, and from this graph schedules and executes the tasks in the distributed infrastructure, taking also care of the required data transfers between nodes.

Modelling and simulation

pyGAlib

GAlib works on adjacency matrices, represented as 2D numpy arrays. This choice certainly limits the size of the networks that the library can handle but it also allows to exploit the powers of numpy to manipulate arrays and boost the performance far beyond pure Python code. As a result, GAlib is simple to use and to extend; easy to read, access and modify. It has no hidden code, so you always know what every function actually does. GAlib includes I/O and statistics tools, and a large set of functions for the analysis of graphs including clustering, distances and paths, matching index, assortativity, roles of nodes in modular networks, rih-club coefficients, K-core decomposition, etc. It also includes functions to generate random networks of different types, randomizing networks, as-well-as many examples and ready-to-use scripts useful also for complete beginners.

Modelling and simulation

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