A MATLAB® toolbox that given a three-dimensional spine reconstruction computes a set of characteristic morphological measures that unequivocally determine the spine shape.
Arbor is a high-performance library for computational neuroscience simulations with multi-compartment, morphologically-detailed cells, from single cell models to very large networks. Arbor is written from the ground up with many-cpu and gpu architectures in mind, to help neuroscientists effectively use contemporary and future HPC systems to meet their simulation needs. Arbor supports NVIDIA and AMD GPUs as well as explicit vectorization on CPUs from Intel (AVX, AVX2 and AVX512) and ARM (Neon and SVE). When coupled with low memory overheads, this makes Arbor an order of magnitude faster than the most widely-used comparable simulation software. Arbor is open source and openly developed, and we use development practices such as unit testing, continuous integration, and validation.
BioExcel Building Blocks Workflows is a collection of biomolecular workflows to explore the flexibility and dynamics of macromolecules, including signal transduction proteins or molecules related to the Central Nervous System. Molecular dynamics setup for protein and protein-ligand complexes are examples of workflows available as Jupyter Notebooks. The workflows are built using the BioBB software library, developed in the framework of the BioExcel Centre of Excellence. BioBBis a collection of Python wrappers on top of popular biomolecular simulation tools, offering a layer of interoperability between the wrapped tools, which make them compatible and prepared to be directly interconnected to build complex biomolecular workflows.
BioExcel-CV19 is a platform designed to provide web-access to atomistic-molecular dynamics trajectories for macromolecules involved in the COVID-19 disease. The BioExcel-CV19 web server interface presents simulated trajectories, with a set of quality control analyses, system information and interactive and graphical information on key structural and flexibility features. All the analyses integrated in the web portal are completely interactive. Whenever possible, a direct link from the analysis to the 3D representation is offered, using the NGL viewer tool. All data produced is available to download from an associated programmatic access API.
BluePyEfe aims at easing the process of reading experimental recordings and extracting batches of electrical features from these recordings. To do so, it combines trace reading functions and features extraction functions from the eFel library. BluePyEfe outputs protocols and features files in the format used by BluePyOpt for neuron electrical model building.
When building a network simulation, biophysically detailed electrical models (e-models) need to be tested for every morphology that is possibly used in the circuit. E-models can e.g. be obtained using BluePyOpt by data-driven model parameter optimization. Developing e-models can take a lot of time and computing resources. Therefore, these models are not reoptimized for every morphology in the network. Instead we want to test if an existing e-model matches that particular morphology `well enough’. This process is called Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information. Next, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination. Finally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches – BluePyMM. This software is also part of the Human Brain Project Brain Simulation Platform.
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.
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.
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.
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.
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.
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.
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