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.
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 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) 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.
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.
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.
Distance-Fluctuation (DF) Analysis is a Python tool that performs analysis of the results of a MD simulation using the Distance Fluctuation matrices (DF), based on the Coordination Propensity (CP) hypothesis. Specifically, low CP values, corresponding to low pair-distance fluctuations, highlight groups of residues that move in a mechanically coordinated way. The script can analyze a MD trajectory and identify the coordinated motions between residues. It can then filter the output matrix based on the distance to identify long-range coordinated motions.
Work through a number of pipelines for single cell model optimization of different brain region cells, run in silico experiments of individual neurons, small circuits and entire brain regions, perform ad hoc data analysis on electrophysiological data, synaptic events fitting, morphology analysis and visualization.
MEDIUM is a Python tool that uses the DF matrices to extract global information on the protein. It works directly on DF images and uses a Convolutional Neural Network (CNN) machine learning approach to train the model in recognising specific patterns in the DF matrix capable of classifying the protein in a specific state in the presence of a ligand (e.g., HOLO or APO states). Then, the trained model can be used to classify the protein state in presence of different ligands, by testing new DF matrices arising from short simulations performed on the new system.
MLCE stands for Matrix of Lowest Coupling Energies and they can be used to represent the region of the protein lowest dynamically coupled. This means that they are also the most prone to be involved in interaction with external partners.
The Hybrid Molecular Mechanics/Coarse-Grained (MM/CG) is a server that helps in the preparation and running of multiscale molecular dynamics simulations of G protein-coupled receptors (GPCR) in complex with their ligands. The Hybrid MM/CG Webserver requires the structure of a GPCR/ligand complex as input and then guides the user through the preparation and running of the simulation.
MoDEL_CNS is a platform designed to provide web-access to atomistic molecular dynamics trajectories for relevant signal transduction proteins. MoDEL_CNS expands the Molecular Dynamics Extended Library (MoDEL) database of atomistic Molecular Dynamics trajectories with proteins involved in Central Nervous System (CNS) processes, including membrane proteins. MoDEL_CNS web server interface presents the resulting trajectories, analyses, and protein properties. All data produced by the project is available to download. MoDEL_CNS will contribute to the improvement of the understanding of neuronal signalling cascades by protein structure-based simulations, calculating molecular flexibility and dynamics, and guiding systems level modelling.
This Python package offers a convenient interface to set-up co-simulation models that simulate TVB large-scale brain network models that interact with NEST spiking neuron models. NEST simulates neural activity at the microscopic spatial scale of single neurons or neuron networks. On the other hand, The Virtual brain simulates at the mesoscopic or macroscopic scales of large neural populations or brain regions. Here, both are brought together to enable neuroscientists to study how these different scales interact and how different scales inform activity "from the bottom up" and "down from the top". A generic Python interface allows users to quickly and conveniently set up a parallel simulation in TVB and in NEST and automatically handles the exchange of currents, spikes, voltages, etc. between the different scales. Although the technical aspect of this tool is realized, the scientific part is a work in progress and we are continuously enriching the coupling between scales such that biophysical plausibility is maintained. The TVB+NEST bundle software package -- available as an easy-to-use Docker image container -- combines the sophistication and flexibility of NEST's spiking neuron simulation infrastructure with TVB's whole-brain simulation, processing, analyses and visualisation capabilities.
NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems, rather than on the exact morphology of individual neurons. It is ideal for networks of any size, including models of information processing (e.g. in the visual or auditory cortex of mammals), models of network activity dynamics (e.g. laminar cortical networks or balanced random networks) and models of learning and plasticity. NEST is openly available for download.
NEST Desktop is a web-based GUI application for NEST Simulator, an advanced simulation tool for computational neuroscience. NEST Desktop enables the rapid construction, parametrization, and instrumentation of neuronal network models. It offers interactive tools for visual network construction, running simulations in NEST and applying visualization to support the analysis of simulation results. NEST Desktop mainly consists of two views and a connection to a server-based NEST instance, which can be controlled using the web-based NEST Desktop front-end. The first view of NEST Desktop enables the user to create point neuron network models interactively. A visual modeling language is provided and a simulation script is automatically created from this visual model. The second view enables the user to analyze the returned simulation results using various visualization methods. NEST Desktop offers additional functionality, such as employing Elephant for more sophisticated statistical analyses.
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.
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