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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The EBRAINS Collaboratory offers researchers and developers a secure environment to work with others. You control the level of collaboration by sharing your projects with specific users, teams or all of the Internet. Many researchers are sharing their work already; several services, tools, datasets, and other resources are publicly available, and many more are available for registered users.
The COMP Superscalar (COMPSs) framework is mainly composed of a task-based programming model which aims to ease the development of parallel applications for distributed infrastructures, such as Clusters, Clouds and containerized platforms, and a runtime system that exploits the inherent parallelism of applications at execution time. The framework is complemented by a set of tools for facilitating the development, execution monitoring and post-mortem performance analysis.
To better understand the relationships between interindividual variability in brain regions’ connectivity and behavioural phenotypes, we are developing a connectivity-based psychometric prediction framework (CBPP). Preliminary to the development of this region-wise machine learning approach, we performed an extensive assessment of the general connectivity-based psychometric prediction (CBPP) framework based on whole-brain connectivity information. Because a systematic evaluation of different parameters was lacking from previous literature, we evaluated several approaches pertaining to the different steps of a CBPP study. We hence tested 72 different approach combinations (3 types of preprocessing x 4 parcellation granularity x 2 connectivity methods x 3 regression methods = 72 combinations) in a cohort of over 900 healthy adults across 98 psychometric variables. Overall, our extensive evaluation combined to an innovative region-wise machine learning approach offer a framework that optimises both, prediction performance and neurobiological validity (and hence interpretability) for studying brain-behaviour relationships.
CoreNeuron supports a reduced set of the functionalities offered by the open source simulator NEURON. The software aims at supporting an efficient and scalable simulation of the electrical activity of neuronal networks that include morphologically detailed neurons. CoreNeuron has been implemented with the goal of minimising memory footprint and obtaining optimal performance, relying on the use of a single MPI process per node and 64 OpenMP threads on IBM BlueGene/Q systems.
Cube, which is used as performance report explorer for Scalasca and Score-P, is a generic tool for displaying a multi-dimensional performance space consisting of the dimensions (i) performance metric, (ii) call path, and (iii) system resource. Each dimension can be represented as a tree, where non-leaf nodes of the tree can be collapsed or expanded to achieve the desired level of granularity. In addition, Cube can display multi-dimensional Cartesian process topologies. The Cube 4.x series report explorer and the associated Cube4 data format is provided for Cube files produced with the Score-P performance instrumentation and measurement infrastructure or the Scalasca version 2.x trace analyzer (and other compatible tools). However, for backwards compatibility, Cube 4.x can also read and display Cube 3.x data.
CxSystem2 is a simulation framework for cortical networks, which operates on personal computers. It is implemented in Python on top of the popular Brian2 simulator, and runs on Linux, Windows and MacOS. There is also a web-based version available via the Human Brain Project Brain Simulation Platform (BSP). CxSystem2 embraces the main goal of Brian – minimizing development time – by providing the user with a simplified interface. While many simple models can be written in pure Brian code, more complex models can get hard to manage due to the large number of biological details. We currently provide two interfaces for constructing networks: a browser-based interface (locally or via the BSP), and a file-based interface (json or csv). Before incorporating neuron models into a network, the user can explore their behavior using the Neurodynlib submodule. Spike output and 3D structure of network simulations can be visualized using ViSimpl, a visualization tool developed by the GMRV Lab.
PostgreSQL relational database schema for tracking the provenance of data for all software components and files involved in the data transformation process. This project provides a Docker container including Alembic and a Python model of the Data Catalog schema to setup and migrate this schema in a target database.
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