Modelling and simulation software

EBRAINS offers technical solutions for brain researchers to conduct sustainable simulation studies. This includes integrated workflows for model creation, simulation and validation, data analysis and visualisation. The simulation engines cover the entire spectrum of levels of description ranging from cellular to network to the whole brain level.

Arbor

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.

Modelling and simulationCellular level simulation

ArDock

ArDock employs the arbitrary docking method to reveal potential interaction sites on the surface of a protein by computationally docking a set of random protein “probes”. The random probes interact in a non-random manner on protein surfaces, and the targeted regions are enriched in biological interfaces. Docking is performed on input protein structures using the Hex software. The ArDock webserver performs the docking calculations and provides tools for the combined analysis of protein structures and sequences and for the visualization of the results to identify interaction sites.

Molecular and subcellular simulation

BioExcel-CV19

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.

Molecular and subcellular simulation

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

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

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.

Molecular and subcellular 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.

Molecular and subcellular simulation

CoreNeuron

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.

Modelling and simulationCellular level simulation

Interactive Workflows for Cellular Level Modeling

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.

Modelling and simulationCellular level simulation

MMCG Webserver

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.

Molecular and subcellular simulation

MoDEL_CNS

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.

Molecular and subcellular simulation

Multi-scale brain simulation with TVB-NEST

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.

Whole-brain simulationModelling and simulation

NEST

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.

Modelling and simulationNetwork level simulationData analysis and visualisation

NEST Desktop

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.

Modelling and simulationNetwork level simulationData analysis and visualisation

NESTML

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.

Modelling and simulationNetwork level simulation

NetPyNE

NetPyNE (Networks using Python and NEURON) is a Python package to facilitate the development, parallel simulation and analysis of biological neuronal networks using the NEURON simulator. Although NEURON already enables multiscale simulation ranging from the molecular to the network level, NEURON for networks, often requiring parallel simulations, requires substantial programming. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.

Modelling and simulationCellular level simulation

NetPyNE GUI

The UI splits the workflows in two tabs available at the top of the screen: define your network and create network. The NetPyNE GUI is implemented on top of Geppetto, an open-source platform that provides the infrastructure for building tools for visualizing neuroscience models and data and for managing simulations in a highly accessible way. The GUI is defined using JavaScript, React and HTML5. This offers a flexible and intuitive way to create advanced layouts while still enabling each of the elements of the interface to be synchronized with the Python model. The interactive Python backend is implemented as a Jupyter Notebook extension which provides direct communication with the Python kernel. This makes it possible to synchronize the data model underlying the GUI with a custom Python-based NetPyNE model. This functionality is at the heart of the GUI and means any change made to the NetPyNE model in the Python kernel is immediately reflected in the GUI and vice versa. The tool’s GUI is available at https://github.com/Neurosim-lab/NetPyNE-UI and is under active development.

Modelling and simulationCellular level 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

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