Tools

Single Cell Model Rebuilder Notebook

The current version of the Single Cell Model ReBuilder Notebook implements a Use Case of the Brain Simulation Platform. It allows to select models obtained in previous optimizations. The user may visualize the electrophysiological features for the chosen model, that will be used as reference by the optimization process, visualize and change parameters of an existing optimization, configure the BluePyOpt optimization algorithm and run the optimization procedure on CSCS and NSG systems. The Use Case allows the user also to choose either a previous optimization from a CSCS container; or choose the result of his/her own optimization from the Collab storage, and then run and save an analysis of the results.

Modelling and simulation

SLURM plugin for the co-allocation of compute and data resources

Using the Job Submit Plugin API of the Slurm Workload Manager this plugin is intended for use in a multi-tiered storage cluster. Having considered two storage tiers, called low performance storage (lps) and high performance storage (hps), this plugin allows for the co-allocation of compute and data resources by passing the job storage requirements of jobs individually.

Data

SNT

SNT is ImageJ's framework for semi-automated tracing, visualization, quantitative analyses and modeling of neuronal morphology distributed with Fiji. SNT supports modern multi-dimensional microscopy data, features advanced visualization and quantification tools, and interacts with all major morphology databases. All aspects of the program can be controlled from a user-friendly interface or programmatically, using several of Fiji's supported scripting languages.

Data analysis and visualisationModelling and simulation

Snudda

Snudda is a tool that allows the user to place neurons within multiple volumes, then performs touch detection to infer where putative synapses are based on reconstructed neuron morphologies. To match experimental pair-wise recordings the putative synapses are then pruned to get the final set of synapses. Using neuron models optimised with BluePyOpt the entire network can be simulated using the NEURON simulator.

Modelling and simulationCellular level simulation

SpectralSegmentation

Spectralsegmentation is a pipeline that can be used to detect active neurons and dendrites in ca-imageing data. A series of steps are defined to achieve this. After image stabilization and transposing an image sequence to the (time x pixel) space, Cross-spectral analysis is applied to low frequency(<1Hz) decimated pixel traces. This results in images representing the cross spectral power of each pixel with it's surrounding pixels at increasing frequency components (0.017Hz steps - 0.4Hz). These images are used to define preliminary ROIs using morphological criteria. The ROIs are then constrained to contain only pixels with possitive correlations. The pipeline includes a graphical user interface to edit the automatically extracted ROIs, to add new ones or delete ROIs by further constraining their properties.

Data analysis and visualisation

SpiNNaker

Simulate or emulate spiking neural networks on SpiNNaker. 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. For real time SpiNNaker simulations, direct use in a neurorobotics simulated environment is also possible.

Neuromorphic computingModelling and simulation

SpykeViewer

It is based on the Neo library, which enables it to load a wide variety of data formats used in electrophysiology. At its core, Spyke Viewer includes functionality for navigating Neo object hierarchies and performing operations on them. A central design goal of Spyke Viewer is flexibility. For this purpose, it includes an embedded Python console for exploratory analysis, a filtering system, and a plugin system. Filters are used to semantically define data subsets of interest. Spyke Viewer comes with a variety of plugins implementing common neuroscientific plots (e.g. rasterplot, peristimulus time histogram, correlogram, and signal plot). Custom plugins for other analyses or plots can be easily created and modified using the integrated Python editor or external editors. Users can download and share additional plugins and other extensions at the Spyke Repository. Among the extensions hosted at the site are plugins for spike detection and spike sorting.

Data analysis and visualisation

sPyNNaker

sPyNNaker is a software package for simulating PyNN-defined spiking neural networks (SNNs) on the SpiNNaker neuromorphic platform. Operations underpinning realtime SNN execution are presented, including an event-based operating system facilitating efficient time-driven neuron state updates and pipelined event-driven spike processing. Preprocessing, realtime execution, and neuron/synapse model implementations are discussed, all in the context of a simple example SNN. Simulation results are demonstrated, together with performance profiling providing insights into how software interacts with the underlying hardware to achieve realtime execution. System performance is shown to be within a factor of 2 of the original design target of 10,000 synaptic events per millisecond, however SNN topology is shown to influence performance considerably. A cost model is therefore developed characterizing the effect of network connectivity and SNN partitioning. This model enables users to estimate SNN simulation performance, allows the SpiNNaker team to make predictions on the impact of performance improvements, and helps demonstrate the continued potential of the SpiNNaker neuromorphic hardware.

Modelling and simulation

SSB Toolkit

The SSB Toolkit is a Python library specifically designed for conducting simulations of mathematical models that represent the signal-transduction pathways of G-protein coupled receptors (GPCRs). This library consists of a set of systems biology simulation routines, enabling the investigation of pharmacodynamic models associated with GPCRs. It provides a means to explore how the structural characteristics of these receptors influence subcellular signaling dynamics.

Modelling and simulationMolecular and subcellular simulation

STEPS

STEPS is a package for exact stochastic simulation of reaction-diffusion systems in arbitrarily complex 3D geometries. Our core simulation algorithm is an implementation of Gillespie's SSA, extended to deal with diffusion of molecules over the elements of a 3D tetrahedral mesh. While it was mainly developed for simulating detailed models of neuronal signaling pathways in dendrites and around synapses, it is a general tool and can be used for studying any biochemical pathway in which spatial gradients and morphology are thought to play a role. STEPS also supports accurate and efficient computational of local membrane potentials on tetrahedral meshes, with the addition of voltage-gated channels and currents. Tight integration between the reaction-diffusion calculations and the tetrahedral mesh potentials allows detailed coupling between molecular activity and local electrical excitability. We have implemented STEPS as a set of Python modules, which means STEPS users can use Python scripts to control all aspects of setting up the model, generating a mesh, controlling the simulation and generating and analyzing output. The core computational routines are still implemented as C/C++ extension modules for maximal speed of execution. STEPS 3.0.0 and above provide early parallel solution for stochastic spatial reaction-diffusion and electric field simulation. STEPS 3.6.0 and above provide a new set of APIs (API2) to speedup STEPS model development. Models developed with the old API (API1) are still supported.

Modelling and simulation

Subcellular model building and calibration tool set

The toolset includes interoperable modules for: model building, calibration (parameter estimation) and model analysis. All information needed to perform these tasks are stored in a structured, human- and machine-readable file format based on SBtab. This information includes: models, experimental calibration data and prior assumptions on parameter distributions. The toolset enables simulations of the same model in simulators with different characteristics, e.g. STEPS, NEURON, MATLAB’s Simbiology and R via automatic code generation.

Modelling and simulationMolecular and subcellular simulation

Subcellular Simulation Webapp

This tool allows import of SBML model files from the subcellular model building and calibration toolset workflow or other external sources. The tool allows users to setup and configure BioNetGen and STEPS simulations. Users can populate mesh models of spines and other neural structures, and run stochastic simulations of signalling pathways.

Modelling and simulationMolecular and subcellular simulation

Subcellular WebApp

The subcellular application was designed as a hub web based environment for creation and simulation of reaction-diffusion models integrated with the molecular repository. It allows also to import, combine and simulate existing models expressed with BNGL and SBML languages. Two types of models are supported: rule-based models convenient and computationally efficient for modeling big protein signaling complexes and chemical reaction network models. The subcellular application is integrated with a number of solvers for reaction-diffusion systems of equations. It supports simulation of spatially distributed systems using STEPS (stochastic engine for pathway simulation) – which provides spatial stochastic and deterministic solvers for simulation of reactions and diffusion on tetrahedral meshes. The application provides as well a number of facilities for visualizing the models geometry and the results of the simulations. The molecular repository is a publicly available database of biological information, relevant for brain molecular network modeling. It accommodates several types of biological information which are not available from existing public databases, such as concentrations of proteins in different subcellular compartments of neuronal and glial cells, kinetic data on protein interactions specific for brain and synaptic signaling and plasticity, data on molecules mobility. The repository is integrated with the subcellular application. They share the same set of entities described by BioNetGen expressions. The molecular repository can be queried from the subcellular application and the results of the query can be added to a molecular network model.

Modelling and simulation

Subcellular Workflow

This workflow has been developed to tackle the challenge of building and analyzing biochemical pathway models, combining pre-existing tools and custom-made software. At the root of our implementation is the Sbtab format, a file format that can store biochemical models and associated data in an easily readable and expandable way.

Modelling and simulation

Surf Ice

Surf Ice is a tool for surface rendering the cortex with overlays to illustrate tractography, network connections, anatomical atlases and statistical maps. While there are many alternatives, Surf Ice is easy to use and uses advances shaders to generate stunning images. It supports many popular mesh formats [3ds, ac3d, BrainVoyager (srf), ctm, Collada (dae), dfs, dxf, FreeSurfer (Asc, Srf, Curv, gcs, Pial, W), GIfTI (gii), gts, lwo, ms3d, mz3, nv, obj, off, ply, stl, vtk], connectome formats (edge/node) and tractography formats [bfloat, pdb, tck, trk, vtk]. Surf Ice uses three stages to draw your image. The first two stages are computed in 3D and create both an image (left column) and a depth buffer (right column). The first stage draws all the items, while the second stage omits the background anatomical image. The final stage uses the 2D outputs of the prior stages. The depth map from the first stage is used to estimate ambient occlusion (SSAO), and the difference between the depth maps from the previous stages allows the software to infer the depth of the overlays behind the background (depth). The SSAO and depth images are composited with the images from the first two stages to generate the final image.

Data analysis and visualisation

Synaptic events fitting Notebook

The Synaptic events fitting Notebook implements a Use Case of the Brain Simulation Platform. Starting from any given model description (.mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally. The traces and the model are stored in the Knowledge Graph. The user can run the fitting procedure using UNICORE authentication on JURECA or on the NSG, check the job status and download and analyse the results.

Modelling and simulation

Synaptic plasticity

This software is developed in a jupyter notebook inside Collaboratory v1 of the HBP. It allows a user to configure and test, through an intuitive GUI, different synaptic plasticity models and protocols on any of the single cell optimized models present in the model catalog. It consists of two tabs: "Config", where the user can specify the plasticity model to use and synaptic parameters such as location, initial weight, activation pattern, additional somatic current injections, or voltage clamp parameter, and "Sim", where the user can define the recording location, weight's evolution, and also the number of simulations to run (to obtain average results). The results are plotted at the end of the simulation and the traces can be downloaded for further analysis.

Modelling and simulation

The fast and parallel Virtual Brain

A fast implementation of The Virtual Brain brain network simulator is written in C using a host of optimizations that make brain simulation faster parallelized (multithreading) containerized (can be conveniently run e.g. through Docker, Shifter or Singularity, without the need to install dependencies or set up environment) uses the Deco-Wang (aka "ReducedWongWang") neural mass model to simulate local brain region activity as described in Deco et al., 2014, Journal of Neuroscience or Schirner et al., 2018, eLife An overview over TVB-on-EBRAINS services is provided in the preprint https://arxiv.org/abs/2102.05888

Modelling and simulation

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