Solutions for brain researchers to conduct sustainable simulation studies and share their results
In research, simulation acts as the main conduit for interchange between experiment and theory. It is a powerful instrument in our endeavour to understand the human brain, which is a complex dynamic system with a multi-scale architecture, further complicated by significant differences between one person’s brain and another’s. The complexity and versatility of the brain, and the variations from one brain to another, are major scientific challenges, driving the development of simulation technology
Neuroscientists have different views on how to best tackle the intricacy of such complex systems, advocating approaches that range from holistic to minimalistic, from the notion that only realistic models can account for the inner workings of our brains, to the assumption that only systematic model simplification will allow us to uncover fundamental principles. Likewise, brain models differ in size, complexity and level of detail. EBRAINS Simulation services offer technical solutions for brain researchers to conduct sustainable simulation studies and build upon prior work, and the means to share their results. The services provide integrated workflows for model creation, simulation and validation, including data analysis and visualisation. The simulation engines cover the entire spectrum of levels of description ranging from cellular to network to whole brain level.
The EBRAINS Cellular Level simulation tool suite includes an ensemble of custom workflows allowing neuroscientists to design, build, simulate and visualise neuronal models; from individual cells to entire brain regions, with cellular-level details. The cell and tissue model construction covers different brain areas and is driven by experimental data that allows in silico exploration of their computational properties and their role in health and disease.
NeuroScheme
Create, navigate, explore and interact with schematic representations of neurons and circuitsNeuroTessMesh
Visualise neural anatomy in great detailViSimpl
Study simulation data and their associated spatial and temporal features by exploratory visual analysisArbor
High-performance library for computational neuroscience simulations, ranging from single-cell models to large networksInteractive Workflows for Cellular Level Modeling
Build, reconstruct, and simulate data-driven brain modelsNEURON /CoreNEURON
A simulator for modelling individual neurons and networks of neuronsNetPyNE
Build, simulate, and analyze data-driven multiscale brain circuit modelsSnudda
Leverage your neuron morphologies and experimental data to create microcircuits with realistic connectivityBrain function emerges at the network level. Even basic functions such as the initial processing of visual input require the interaction of many neurons in different brain areas. Network-level simulation technology enables investigation of such brain-scale networks, covering multiple brain areas at the resolution of single neurons and synapses. The direct link to brain function makes network-level simulation attractive for AI and robotics applications. To enable brain-scale simulations, the technology needs to be extremely scalable and capable of exploiting modern supercomputers. The simplifications that underpin network-level simulation technology have also inspired the design of neuromorphic systems.
Neuromorphic Computing
Simulate or emulate spiking neural networks with neuromorphic compute systemsNeurorobotics Platform
Simulating intelligent agents within realistic environmentsElephant
Analyse the neuronal dynamics of experiments and brain simulations with this Electrophysiology Analysis ToolkitNEST Desktop
Construct neuronal networks and explore network dynamics with the NEST Simulator GUINEST Simulator
A simulator for spiking neural network models of any sizeNESTML
A domain-specific language to write custom neuron modelsPyNN
A simulator-independent language for building neuronal network modelsBrain Scaffold Builder
Flexible and organized workflows for neural circuit reconstruction and simulation at different levels of detailTo simulate whole brains, the white-matter axon fibre bundle network, the so-called structural connectome, is reconstructed to define the model network. To reduce complexity and computational demands, the activity of brain regions is typically not simulated by networks of individual neurons. Instead, mean-field theory is used to replicate the main dynamics of large groups of neurons. The resulting models of the different networks are connected by the weights and time delays of reconstructed structural connectomes. Whole brain simulations have been used to explain several empirically observed phenomena, such as the emergence in fMRI of functional networks, the spread of epileptic seizures or relationships between phenomena observed in fMRI and electrical neural activity over several temporal scales. In EBRAINS, the simulation tools at the whole brain level are developed around the framework of The Virtual Brain (TVB).
The Virtual Brain
Create personalised brain models and simulate multi-scale networksData analysis provides methods and workflows to reveal hidden dynamics and extract characteristic statistical features obtained from simulations and experiments. These toolboxes help computational neuroscientists analyse simulations of neuronal dynamics and assist in validating models against experimental evidence. Visualisation provides scientists with a powerful tool for interactive data analysis, from an overview to deep insights. EBRAINS offers access to interactive tools for visualisation of models and simulation results, from the cellular to the network level.
NeuroScheme
Create, navigate, explore and interact with schematic representations of neurons and circuitsNeuroTessMesh
Visualise neural anatomy in great detailViSimpl
Study simulation data and their associated spatial and temporal features by exploratory visual analysisElephant
Analyse the neuronal dynamics of experiments and brain simulations with this Electrophysiology Analysis ToolkitNEST Desktop
Construct neuronal networks and explore network dynamics with the NEST Simulator GUISubcellular Modeling and Simulation services
Set up subcellular models, constrain these from quantitative data, and run simulations.PyNN represented on EBRAINS
Releases of NEST 3.0 and accompanying Education Frontend (NEST Desktop 3.0), and of NESTML 4.0 with ODE-toolbox 2.3
Molecular and subcellular level simulation tools and services represented on EBRAINS
First use-case workflow for co-simulation using the standardized framework on local machines
Release of the first version of the visualization framework’s documentation including a description of an example curricula for the Education Frontend (NEST Desktop)
Release of PyNN 0.10.0 supporting NEST 3.0 and NEURON 8.0
Complete migration of BSP use cases to the cellular level simulation workflows and use cases page on EBRAINS
Co-simulation services represented on EBRAINS, and HPC-deployed use case for co-simulation using standard framework available
Release of the second version of the visualization framework’s documentation
Alpha version of NRP and co-simulation workflow available
Improved cellular level simulation workflows and use cases to take into account user requests
Finalized workflow ensuring that all parts of NEST documentation are complete
Support for third-factor learning rules in NESTML
Releases of PyNN 1.0.0 (stable API) and Elephant 1.0 (stable API and visualization)
Upgraded cellular level simulation workflows and use cases for interaction with EBRAINS
Initial integration of TVB with the data representation and communications standard for simulation defined in EBRAINS for co-simulation on HPC
Release of the visualization framework with documentation including tutorials and integration into EBRAINS
Common user-level documentation for data analysis and model-validation components available
Simulation and analysis workflows for each simulation scale and co-simulation, including integration in EBRAINS infrastructure, documentation, validation, visualization where appropriate, integration testing and user support workflows
Completed use-case based validation of visualization framework
NESTML supporting SpiNNaker platform code generation
Release of PyNN 2.0.0 supporting multi-compartmental modelling with NEURON and Arbor
Two-way translation and control module ready for neural mass to point-neuron models