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Cellular level simulation

The EBRAINS Cellular Level simulation tool suite includes 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. It is driven by experimental data that allows in silico exploration of their computational properties and their role in health and disease.


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


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


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


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 and is under active development.

Modelling and simulationCellular level simulation


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


NeuroScheme is a tool that allows users to navigate through circuit data at different levels of abstraction using schematic representations for a fast and precise interpretation of data. It also allows filtering, sorting and selections at the different levels of abstraction. Finally it can be coupled with realistic visualization or other applications using the ZeroEQ event library developed in WP 7.3. This application allows analyses based on a side-by-side comparison using its multi-panel views, and it also provides focus-and-context. Its different layouts enable arranging data in different ways: grid, 3D, camera-based, scatterplot-based or circular. It provides editing capabilities, to create a scene from scratch or to modify an existing one.

Modelling and simulationCellular level simulationData analysis and visualisation


Visualise neurons and neural circuits consisting of a large number of cells with NeuroTessMesh on your desktop. It enables the visualisation of the 3D morphology of cells included in open databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. NeuroTessMesh takes morphological tracings of cells acquired by neuroscientists as its only input. It generates 3D models that approximate the neuronal membrane. The resolution of the models can be adapted at the time of visualisation. NeuroTessMesh can assign different colours to different morphologies, in order to visually codify relevant morphological variables, or even neuronal activity.

Modelling and simulationCellular level simulationData analysis and visualisation


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


ViSimpl involves two components: SimPart and StackViz. SimPart is a three-dimensional visualizer for spatio-temporal data that allow spatio/temporal analysis of the simulation data, using particle-based rendering. StackViz illustrates how the electrophysiological variables evolve over time and provides a temporal representation of the data at different aggregation levels. They allow users to visually discriminate the activity of different groups of neurons, and provide detailed information about individual neurons of interest. These components share synchroniszed playback control of the simulation being analyzsed and work together as linked views, although they are loosely coupled and can also be used independently. They are ready to be used with BlueConfig Datasets among other file formats such as specific HDF5 and CSV. VisSimpl can be coupled with NeuroScheme for adding functionality such as navigate through the underlying structure of the data using symbolic representations and different levels of abstraction.

Modelling and simulationCellular level simulationData analysis and visualisation

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