Tools

BluePyOpt

The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures.

Modelling and simulation

BluePySNAP

Blue Brain Simulation and Neural network Analysis Productivity layer (Blue Brain SNAP). Blue Brain SNAP is a Python library for accessing BlueBrain circuit models represented in SONATA format.

BrainScaleS

Simulate or emulate spiking neural networks on BrainScaleS. Models and simulation experiments can be described in a Python script using the PyNN API and submitted either through the EBRAINS Collaboratory website or via our web API (python client available). Results can be viewed via browser and downloaded as data files for analysis, making use e.g. of the data analysis capabilities EBRAINS offers.

Neuromorphic computingModelling and simulation

BrainScaleS-OS

The BrainScaleS Operating System (BrainScaleS OS) is a software stack designed for the user-friendly operation of the BrainScaleS spiking neuromorphic computing architectures.

Brain Simulation Platform Service Account

The Brain Simulation Platform Service Account allows developers to submit jobs, on behalf of the HBP Collaboratory users, on the remote HPC systems made available in the HBP framework. The service leverages the UNICORE APIs and uses the token generated for individual users (upon logging in on the HBP Collaboratory) for tracking general information on job submission and status.

Modelling and simulation

Brainstorm

Brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal electrophysiology. *Any time you want to use Brainstorm, be sure to pull the latest version from their GitHub Repository first.** Our objective is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. For physicians and researchers, the main advantage of Brainstorm is its rich and intuitive graphic interface, which does not require any programming knowledge. We are also putting the emphasis on practical aspects of data analysis (e.g., with scripting for batch analysis and intuitive design of analysis pipelines) to promote reproducibility and productivity in MEG/EEG research. Finally, although Brainstorm is developed with Matlab (and Java), it does not require users to own a Matlab license: an executable, platform-independent (Windows, MacOS, Linux) version is made available in the downloadable package. To get an overview of the interface, you can watch this introduction video.

Data analysis and visualisation

Brayns

Brayns is a visualizer that can interactively perform high-quality and high-fidelity rendering of neuroscience large data sets. It provides an abstraction of the underlying rendering engines, so that the best possible acceleration libraries can be used for the relevant hardware (CPU or GPU). Thanks to its client/server architecture, Brayns can be run in the cloud as well as on a supercomputer and stream the rendering to any browser, either in a web UI or a Jupyter notebook.

Data analysis and visualisation

Brion

Brion provides two libraries Brion and Brain. The former is a collection of file readers and writers intended for low level access to the data model. The latter is a set of higher level classes that wrap low level data objects with a use-case oriented API. IO library This is the core library provided by Brion. It includes classes for reading and writing files which store the Blue Brain data model. * Fast and low-overhead read access to: * Blue configs (brion::BlueConfig) * Circuit description (brion::Circuit) * H5 Synapses data (brion::SynapseSummary, brion::Synapse) * Target (brion::Target) * BBP binary meshes (brion::Mesh) * BBP H5 morphologies and SWC morphologies (brion::Morphology) * Compartment reports (brion::CompartmentReport) * Spike reports (brion::SpikeReport) * Fast and low-overhead write access to: * Compartment reports (brion::CompartmentReport) * BBP binary meshes (brion::Mesh) * BBP H5 morphologies (brion::Morphology) * Spike reports (brion::SpikeReport) * Basic data types to work with the loaded data using Boost, Lunchbox ### High level library The higher level library is called Brain and it provides: * brain::Circuit to facilitate loading information about cells, morphologies (in local and global circuit coordinates) and synapses. * brain::neuron::Morphology with higher level functions to deal with morphologies. * brain::Synapses and brain::Synapse for array and object access to synapses.

Modelling and simulation

Brain Scaffold Builder

The BSB is a framework for reconstructing and simulating multi-paradigm neuronal network models. It removes much of the repetitive work associated with writing the required code and lets you focus on the parts that matter. It helps write organized, well-parametrized and explicit code understandable and reusable by your peers. This package is intended to facilitate spatially, topologically and morphologically detailed simulations of brain regions developed by the Department of Brain and Behavioral Sciences at the University of Pavia.

Network level simulationModelling and simulation

bsp-usecase-wizard

Web application that displays scientific use cases and creates the environment that allows end users to run them flawlessly. Different categories of use cases like morphology analysis, trace analysis, simulation and different scales such as brain area, single cell, etc. are shown to be run in Ebrains infrastructure. Some of the use cases are web apps, others jupyter notebooks. This is part of Interactive Workflows for Cellular Level Modeling in Ebrains

Central Nervous System bioactive compounds

Central Nervous System (CNS) is a platform designed to efficiently generate and parameterize bioactive conformers of ligands binding to neuronal proteins. The project is part of the Parameter generation and mechanistic studies of neuronal cascades using multi-scale molecular simulations of the Human Brain Project. CNS conformers are generated using a powerful multilevel strategy that combines a low-level (LL) method for sampling the conformational minima and high-level (HL) ab-initio calculations for estimating their relative stability. CNS database presents the results in a graphical user interface, displaying small molecule properties, analyses and generated 3D conformers. All data produced by the project is available to download. CNS will contribute in the improvement of the understanding of neuronal signalling cascades by protein structure-based simulations, calculating thermodynamics and kinetics constants of the molecular processes.

Modelling and simulation

Central Nervous System ligands

Central Nervous System (CNS) ligands is a platform designed to efficiently generate and parameterize bioactive conformers of ligands binding to neuronal proteins. CNS conformers are generated using a powerful multilevel strategy that combines a low-level (LL) method for sampling the conformational minima and high-level (HL) ab-initio calculations for estimating their relative stability.CNS database presents the results in a graphical user interface, displaying small molecule properties, analyses and generated 3D conformers. All data produced is available to download. CNS ligands provides important data for workflows for parameter generation and mechanistic studies of neuronal cascades using multi-scale molecular simulations in the Human Brain Project.

Modelling and simulationMolecular and subcellular simulation

CerebTests

A SciUnit library for data-driven testing of cerebellum models. CerebTests is one of the four components of CerebUnit, the others being CerebModels, CerebData and CerebStats.

CGMD

Advances in coarse-grained molecular dynamics (CGMD) simulations have extended the use of computational studies on biological macromolecules and their complexes, as well as the interactions of membrane protein and lipid complexes at a reduced level of representation, allowing longer and larger molecular dynamics simulations. Here, we present a computational platform dedicated to the preparation, running, and analysis of CGMD simulations. The platform is built on a completely revisited version of our Martini coarsE gRained MembrAne proteIn Dynamics (MERMAID) web server, and it integrates this with other three dedicated services. In its current version, the platform expands the existing implementation of the Martini force field for membrane proteins to also allow the simulation of soluble proteins using the Martini and the SIRAH force fields. Moreover, it offers an automated protocol for carrying out the backmapping of the coarse-grained description of the system into an atomistic one.

Modelling and simulation

CGMD Platform

The Coarse-grained Molecular dynamics(CGMD) platform is a publicly available web server for preparing and running coarse-grained molecular dynamics simulations using different force-fields. The input file is a protein structure. The user is guided through the preparation of the systems, either in a membrane or in solvent, and the running of short simulations following standard protocols.

Modelling and simulationMolecular and subcellular simulation

ChemBioServer

ChemBioServer is a publicly available web application for effectively mining and filtering chemical compounds used in drug discovery. It provides researchers with the ability to (i) browse and visualize compounds along with their properties, (ii) filter chemical compounds for a variety of properties such as steric clashes and toxicity, (iii) apply perfect match substructure search, (iv) cluster compounds according to their physicochemical properties providing representative compounds for each cluster, (v) build custom compound mining pipelines and (vi) quantify through property graphs the top ranking compounds in drug discovery procedures. ChemBioServer allows for pre-processing of compounds prior to an in silico screen, as well as for post-processing of top-ranked molecules resulting from a docking exercise with the aim to increase the efficiency and the quality of compound selection that will pass to the experimental test phase.

Data analysis and visualisation

Clint Explorer

Clint Explorer is an application that uses supervised and unsupervised learning techniques to cluster neurobiological dataset. The main contributions of this software is that incorporates the expert’s know-how in the clustering process. Besides, it allows to interpret the results providing different metrics.

Data analysis and visualisation

Collaborative Brain Wave Analysis Pipeline

The pipeline ingests data from multiple measurement types of spatially organized neuronal activity, such as ECoG or calcium imaging recordings. The pipeline returns statistical measures to quantify the dynamic wave-like activity patterns found in the data. Individual parts of the snakemake-based pipeline are fully configurable. The composition of Cobrawap elements can be adapted to various datasets through by means of a modular design of self-contained sequential stages composed of multiple atomic blocks.

Validation and inference

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