Accelerate Your Neuroscience Research with EBRAINS

EBRAINS tutorials and users day - 12 March 2025 in Heidelberg (Germany)

The EBRAINS tutorials and users day is for two audiences: newcomers to the EBRAINS Research Infrastructure (introductions, overviews, beginner hands-on tutorials) and seasoned EBRAINS users for advanced tutorials and user discussion groups with developers.

• Quick demonstration of a sample set of "EBRAINS science tools in action" in the morning plenary talks (this part of the event can also be attended online free of charge)
• Followed by parallel hands-on tutorials: meeting participants can attend 3 consecutive sessions and choose for each from a set of in parallel tutorials. This part is onsite only for most tutorials.
  
  

Registration

• for attending on-site in Heidelberg (Germany) -- registration fee (coffee breaks, catering lunch and dinner at the meeting site, ...): 40 Euro for student attendants (Bachelor, Master, PhD students) / 80 Euro for everybody else.
  • Non-project members:
    • We strongly recommend to register with an EBRAINS account, as the account gives access to the services presented and an account will be needed to use the EBRAINS RI hands-on
      • get an EBRAINS account HERE (free of charge)
      • then register with EBRAINS account (with registration fee for the meeting)
    • It is also possible to register without an EBRAINS account (use this option primarily, if your email domain is not (yet) on the positive-list for immediate EBRAINS account creation)
  • EBRAINS2.0 project members please register here
    
    
• for attending on-line (via zoom) -- free of charge:
  • register for on-line attendance with EBRAINS account (recommended)
  • or register for on-line attendance without EBRAINS account

Venue

European Institute for Neuromorphic Computing (EINC),
Im Neuenheimer Feld 225a,
D-69120 Heidelberg,
Germany

Airports

• Nearest airport: Frankfurt international airport (FRA)
  • Train Station in the airport itself: Frankfurt Flughafen Fernbahnhof (use bahn.de for time table information)
  • About an hour by car. Info: we often used the TLS shuttle service in the past. A shuttle for up to 7 people costs around 160 Euro single trip. This shuttle needs to be booked in advance.
• also relatively close: Stuttgart airport (STR) Train station in the airport: "Stuttgart Flughafen/Messe". (use bahn.de for time table information)

Train station

Heidelberg Main Station (Heidelberg Hauptbahnhof, use bahn.de for time table information)

From Heidelberg Main station to the institute:

• 25 min walking distance
• or 5 min by tram (Tram number 24, direction: Handschuhsheim Burgstraße. The tram departs from "Steig E" outside the station building. See the plan of Heidelberg Main station.
  Leave the tram at "Bunsengymnasium", then walk 3 min to the institute)

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Option for participants with young children (age 1.5 -6 years)

We can book child care (for children age 1.5 to 6 years) at the universities KidsClub, 7 min walking distance from the meeting location. This option is (only) available until end of January (and only for children with both measles vaccinations completed). Please contact kindler@kip.uni-heidelberg.de for this option.

A high level introduction for researchers into the possibilities, tools and workflows offered by the EBRAINS Research Infrastructure today = for immediate use in own research projects

Briefly showing some of the tools / services / hardware systems or software environment, which are part of the EBRAINS research infrastructure in action -- "teasers" for the tutorials, starting after the coffee break.

The EBRAINS Knowledge Graph:

• Documentation
• Search

Introductory tutorial

In this tutorial, we will learn how to search the EBRAINS Knowledge Graph and download data. We will introduce you to the openMINDS schemas and explain how metadata is organized in EBRAINS. Next, we will guide you through practical use cases, demonstrating how you can work with this data.

Participants will need an EBRAINS account, and at least a basic knowledge of Python programming.

Extended tutorial

This is a continuation of the introductory tutorial, covering further practical use cases.

Advanced tutorial

This tutorial will cover more advanced use of the EBRAINS Knowledge Graph, including more complex queries, uploading of metadata to the KG, and incorporating KG access into your own software.

Participants should already be familiar with the openMINDS metadata framework, either from attending the introductory tutorial or from reading the documentation, and should be able to program in either Python, Javascript, MATLAB or Java. Participants will need an EBRAINS account.

We will present Arbor, a multicompartmental simulation library, that complements NEST, TVB, and nanoscale simulations, as well as offering interfaces to these tools. Arbor has been designed to leverage modern hardware, including GPUs, while delivering an intuitive interface to neuroscientists that is isolated from the concrete, low-level details. It has been shown to deliver performance up to the full scale of JUWELS booster. In this tutorial, we will show how to use Arbor starting from a simple ring network in NEST that is transformed, step by step, into a morphologically detailled model. Participants will be given ample chance to interact the models.

Note: there are also two specific NEST related tutorials offered (see below):

• NESTML tutorial
• NEST astrocyte module tutorial

Tutorial offered: a beginners online hands-on experience on using NEST Desktop for teaching

NEST is a well known numeric spiking neural network simulator. NEST desktop brings the simulator to the browser for easy entry first-steps contact with NEST.

In this tutorial, participants will be able to construct and simulate Spiking Neural Networks directly on the SpiNNaker hardware using the EBRAINS JupyterLab platform. They will learn how to program networks using the PyNN SNN language, and how the PyNN constructs work on the SpiNNaker platform. They will then get to try out these networks themselves and see the results from the simulations, as well as ask any other questions about SpiNNaker and how they might use it to explore SNNs in their own work.

The QUINT workflow takes you through steps to quantify and analyse labelled features within a known atlas space (2D rodent histological section images). The QUINT workflow comprises a suite of software designed to support atlas-based quantification. All the software have user interfaces, with no coding ability required. It generates object counts and percentage coverage per atlas-region, in addition to point clouds for visualising the features in 3D. For details see: https://www.ebrains.eu/brain-atlases/analysis/labelled-features-analysis

A prerequisite for using the workflow hands-in is to bring an own laptop with Windows installed and be prepared to install several software packages (it is rather quick to download and install them).

For downloading and installation instructions of the desktop versions, all instructions can be found on the website:

• as an overview
• For individual tools, you can find the “card” and the online manual from the quicknii card or the https://quicknii.readthedocs.io/en/latest/

This tutorial will focus on the use of protein structural information to explore and predict molecular interactions and to estimate kinetic parameters (https://www.ebrains.eu/modelling-simulation-and-computing/simulation/molecular-and-subcellular-simulation). This information can be used to support the mathematical modeling of molecular signalling networks, particularly in studies of brain function and neurological disorders.

Following an overview of different approaches, the tutorial will focus on (1) τRAMD, a computationally efficient procedure that enables the computation of relative residence times (τ) or dissociation rates of protein-ligand complexes, and (2) SDA (Simulation of Diffusional Association), a Brownian dynamics simulation software package for the simulation of the diffusion of biomacromolecules in aqueous solution that can be used to compute bimolecular diffusional association rate constants.

Tutorials offered: Introductory level and Advanced level.

Snudda (github link) builds networks of neurons with synaptic connectivity based on the morphologies of reconstructed neurons and touch detection. The detected synapses are pruned to match experimentally measured pairwise connectivity. The network connectivity can be exported, or directly simulated using NEURON.

Understanding the human brain requires access to experimental data that capture relevant aspects of brain organization across a broad range of scales and modalities, and typically originate from a plethora of resources. To make multimodal and multidimensional measures of brain organization accessible, they need to be integrated into a common reference framework and exposed via suitable software interfaces. This tutorial will introduce participants to siibra toolsuite, which provides access to a multilevel atlas of the human brain built from “big data”. The atlas integrates brain reference templates at different spatial scales, complementary parcellation maps, and a wide range of multimodal data features. It links macroanatomical concepts and their inter-subject variability with measurements of the microstructural composition and intrinsic variance of brain regions, using cytoarchitectonic maps as a reference, and integrating the BigBrain model as microscopic reference template. The tool suite includes a web-based 3D viewer (siibra-explorer) and a Python library (siibra-python) to support a broad range of neuroscientific use cases. It makes use of EBRAINS as a data sharing platform and cloud infrastructure and implements interfaces to other neuroscience resources. The focus of this tutorial will be on building reproducible workflows with BigBrain data using the siibra-python library.

Tutorials offered: Beginners level tutorial and a machine-learning methods related tutorial.

Beginner level tutorial: In this tutorial, participants will get individual remote access to the BrainScaleS-2 platform. They will learn how to program the system, explore its analog properties and create spiking neural networks that learn to solve user-defined tasks. The session will be suitable for building basic knowledge about neuromorphic processors and analog computing as well as for kickstarting complex scientific research on BrainScaleS-2.

Machine learning methods related tutorial: Recent advances in machine learning with neural networks require an increasing energy budget. Neuromorphic hardware platforms, such as the BrainScaleS-2 (BSS-2) system, aim to offer accelerated energy-efficient computing solutions through analog computation. However, scalable training of spiking neural networks (SNNs) on an analog substrate remains challenging. Our jaxsnn software framework (based on JAX) enables data-efficient event-based gradient computation, tailored for optimizing networks on hardware operating on event data. In this hands-on tutorial, participants will first learn how to use jaxsnn on BrainScaleS for implementing “Spiking Heidelberg Digits” (SHD) spoken digit classification with a spiking neuronal network on the BSS-2 system.

We also offer full-length tutorials for these tools -- EBRAINS has more tools than short introduction talks fit into the morning...

Interactively build and optimize your own data-driven, biophysically detailed neuron model, via EBRAINS services and HPC facilities.

Tutorial offered: Introductory level

Biophysically detailed neural models help understand physiological mechanisms of individual cells and cell ensembles. In order to build a detailed model of a single neuron, the neuron morphology and the ion channel dynamics must be provided. Furthermore, a set of electrophysiological data is needed to fine tune the model parameters, if one wants to replicate the experimental observations.

In order to build and optimize biophysically detailed models of individual neurons, we have developed the Hodgkin-Huxley Neuron Builder (HHNB, hhnb.ebrains-italy.eu/hh-neuron-builder/) web application, where models, created via the NEURON simulation framework, are optimized against experimental recordings. Users can choose pre-existing models and data from the EBRAINS catalogues or provide their own. In addition, we have integrated the HHNB with the HippocampusHub (HH, hippocampushub.eu) where hippocampal morphologies, channel dynamics and neural signals can be selected from public repositories (e.g., neuromorpho.org, bluebrainnexus.io, modeldb.science) The focus of this turorial will be on the entire model building and optimization workflow, via the HH and the HHNB, from component selection to model simulation.

URL - (more details will be added to the agenda)

NESTML is a domain-specific language for neuron and synapse models.

The NESTML toolchain automatically generates high-performance code for these models, allowing them to be used in simulations of brain activity on several platforms, in particular the NEST Simulator running on HPC, but also the SpiNNaker neuromorphic platform. In this interactive tutorial, we will learn how to create a custom neuron model that exhibits dendritic action potentials, and combine it with a custom synaptic plasticity rule to create a network that learns and replays specific sequences of stimuli.

This tutorial features a session on a hyper-parameter optimization framework, called L2L1, implementing the concept of learning to learn.

The framework provides a selection of different optimization algorithms and makes use of multiple high performance computing back-ends (e.g., multi nodes, GPUs) to do vast parameter space explorations in an automated and parallel fashion (Yegenoglu et al. 2022). During this session you will learn about the installation and use of the framework within the EBRAINS’ Collaboratory.

Two use-cases will be explored during the tutorial including NEST and TVB. Participants will learn how to launch the simulations on High Performing Computing infrastructure, deploying jobs on CPUs and GPUs making use of UNICORE from the EBRAINS’ Collaboratory.

In this introductory level tutorial we will introduce participants to analyzing electrophysiological data from simulation and experiment using the tools Neo (https://neuralensemble.org/neo) for representing and handling the data, and Elephant (https://python-elephant.org) for performing the actual analysis. We will cover using the basic data objects, loading a piece of realistic data and performing various data manipulations and analyses. For the tutorial, participants will be able to execute tutorials on their laptops with minimal installation effort (assuming working Python environment) or using the EBRAINS Collaboratory.

(This tutorial might be of interest also for science school teachers in grade 8 and above to determine, whether some 'real research tools' could also be used in their teaching setting) EBRAINS tools for teaching brochure and overview poster

Several of the EBRAINS RI tools are especially suited for use also in teaching settings like university courses and some of them even on school level (8th grade and above).

In this tutorial several of those tools suitable for use in teaching settings will be briefly demonstrated.

Tool tutorial registrations will be collected in the registration step. We will then distribute the hands-on tutorials to the 3 parallel slots to maximize the possibility for attendants to visit their preferred tool tutorials.