Tutorials & E-Library

For the purpose of exploratory simulations, we created a program that sets up the model network and provides just enough of a graphical user interface to launch a simulation and display results. This program is used to run simulations in order to verify that the model is implemented properly, and that the model generates enough spikes to validate the segmentation and reconstitution of a complete simulation.

In this notebook we will look more closely at the EBRAINS dataset "[Excitability of mouse hippocampal CA1 pyramidal neurons during sustained (500 ms) strong depolarization (v1)](https://search.kg.ebrains.eu/#07554ebd-95a2-46f0-8065-d961d56ce098)", contributed by Machhindra Garad while at Otto-von-Guericke University, Magdeburg, Germany and Volkmar Lessmann from the Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.

As we can see from the dataset description,

<i>This dataset provides an analysis of electrophysiological feature often observed in recordings from mouse CA1 pyramidal cells that has so far been ignored by experimentalists and modelers. It consists of a large and dynamic increase in the depolarization baseline (i.e., the minimum value of the membrane potential between successive action potentials during a sustained depolarizing input) in response to strong somatic current injections.</i>
More information is available in the [Data Descriptor](https://search.kg.ebrains.eu/instances/07554ebd-95a2-46f0-8065-d961d56ce098).

This dataset forms part of the results reported in:

Bianchi, D., Migliore, R., Vitale, P., Garad, M., Pousinha, P. A., Marie, H., Lessmann, V., & Migliore, M. (2022). Membrane electrical properties of mouse hippocampal CA1 pyramidal neurons during strong inputs. *Biophysical Journal*, **121**(4), 644–657. https://doi.org/10.1016/j.bpj.2022.01.002

In this notebook we will demonstrate how to access the data files and the metadata from this study, and how these data can be analysed and visualised, with the goal of reproducing some of the figures from the article.

In this notebook we will look more closely at the EBRAINS dataset "[Excitability of mouse hippocampal CA1 pyramidal neurons during sustained (500 ms) strong depolarization (v1)](https://search.kg.ebrains.eu/#07554ebd-95a2-46f0-8065-d961d56ce098)", contributed by Machhindra Garad while at Otto-von-Guericke University, Magdeburg, Germany and Volkmar Lessmann from the Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.

As we can see from the dataset description,

<i>This dataset provides an analysis of an electrophysiological feature often observed in recordings from mouse CA1 pyramidal cells that has so far been ignored by experimentalists and modelers. It consists of a large and dynamic increase in the depolarization baseline (i.e., the minimum value of the membrane potential between successive action potentials during a sustained depolarizing input) in response to strong somatic current injections.</i>
More information is available in the [Data Descriptor](https://search.kg.ebrains.eu/instances/07554ebd-95a2-46f0-8065-d961d56ce098).

This dataset forms part of the results reported in:

Bianchi, D., Migliore, R., Vitale, P., Garad, M., Pousinha, P. A., Marie, H., Lessmann, V., & Migliore, M. (2022). Membrane electrical properties of mouse hippocampal CA1 pyramidal neurons during strong inputs. *Biophysical Journal*, **121**(4), 644–657. https://doi.org/10.1016/j.bpj.2022.01.002

In this notebook we will demonstrate how the data from this study can be analysed and visualised, with the goal of reproducing some of the figures from the article. This version of the notebook does not use metadata from the EBRAINS Knowledge Graph, and so can be run without internet access once the initial dataset download is complete.

Brain atlases enable the localization and analysis of data from different sources in a common reference system, making them an essential research tool for understanding the structural and functional organization of the brain. EBRAINS offers a multilevel atlas of the human brain, which captures different principles of brain organization in a comprehensive anatomical framework. It integrates the Julich-Brain probabilistic cytoarchitectonic maps and the BigBrain microscopic 3D model as core elements, and links them with multimodal data features describing microstructure, connectivity and function. The atlas is deeply integrated into the EBRAINS infrastructure, making use of its sustainable data sharing capabilities and cloud resources. This session provides a conceptual introduction to the multilevel human brain atlas, and an overview of the EBRAINS research infrastructure as a sustainable platform for accessing, operating and developing the atlas.

Links:
The Multilevel Human Brain Atlas: https://atlases.ebrains.eu/
Julich Brain Atlas https://julich-brain-atlas.de
LinkedIn: / julich-brain-atlas

EBRAINS is an open, digital research infrastructure (RI) where scientific researchers, clinicians, and other experts from various disciplines converge to share, explore and reuse complex, heterogeneous data in the field of neuroscience. At its core, EBRAINS aims to establish a user-friendly FAIR data and knowledge implementation in a centralized management system, called EBRAINS Knowledge Graph (KG), that empowers other services and researchers to seamlessly connect and integrate various neuroscience data into a suite of specialized tools, computational models, and workflows. This session is divided into two parts. The first part will introduce how to interactively and programmatically explore and reuse data and knowledge shared through EBRAINS. It will cover demonstrations of the search and filter functionalities of the KG Search User Interface (UI), and corresponding programming patterns in the KG Core Python Software Development Kit (SDK). The second part will provide an overview on how to share data and knowledge through EBRAINS via its curation service. It will demonstrate common data management principles, guidelines for data descriptors, and linked data representations in database systems. For the latter, the session will give an introduction to the openMINDS metadata framework, and demonstrate respective tooling for entering linked data representations into the EBRAINS KG.

Understanding how the brain works is one of the grand challenges in science and requires the integration of huge amounts of heterogeneous and complex data. Numerous research publications present experimental data at various levels of granularity and describe a wide range of structural and functional aspects of the brain. In this context, reference atlases of the brain are important tools for assigning location to data captured with the many methods and instruments used to study the brain. With a new generation of three-dimensional digital reference atlases, new solutions for integrating and disseminating brain data are being developed. During the talk, possibilities will be shown to explore brain regions in an interactive 3D viewer, as well as ways to spatially anchor your own volumetric data in a high-resolution reference space. Furthermore, it will be presented how to access linked multimodal data features from the EBRAINS Knowledge Graph with published and freely available tools. The steps and tools shown will be applied in practice in a subsequent hands-on session "Browsing reference atlases online" (~30min).

Hands-on: Browsing reference atlases online

The first level of HBP data integration is achieved by spatial mapping of all data into a common anatomical reference atlas space. This mapping can be semantic by use of specific atlas structure names, or spatial by registration of image data to a 3-D reference atlas template. The hands-on session, will demonstrate the spatial integration of a Hippocampus dataset to the high-resolution BigBrain reference space using voluba. Furthermore, browser-based interaction with different atlases, reference spaces and data features will be showcased using the interactive atlas viewer siibra-explorer.

Links:

[The Multilevel Human Brain Atlas]( https://atlases.ebrains.eu/)
[Julich Brain Atlas] (https://julich-brain-atlas.de)
[LinkedIn: julich-brain-atlas](/julich-brain-atlas)
[siibra-explorer](https://atlases.ebrains.eu/viewer/)
[voluba Webservice](https://voluba.apps.hbp.eu)
[voluba Documentation](https://voluba.readthedocs.io)
[exercises](https://go.fzj.de/siibra-explorer-pdf)

siibra is a software tool suite that implements a multilevel atlas of the brain by providing streamlined access to reference templates at different spatial scales, complementary brain parcellations maps, and multimodal regional data from different sources which is linked to brain anatomy at different spatial scales. It addresses interactive exploration via an interactive 3D web viewer (siibra-explorer) and integration into data analysis and simulation workflows with a comprehensive Python library (siibra-python), supporting a broad range of workflows for anatomists, experimentalists and computational neuroscientists with varying experience levels, from beginners to those with a solid background in Python. This session offers participants an immersive opportunity to explore the advanced tools and techniques for data analysis and visualization. We will briefly introduce the tool suite and highlight its features and benefits. Participants will learn to access 3D reference templates and maps, including anatomical, and connectivity atlases. We will interactively explore BigBrain cytoarchitectonic maps and cortical layer segmentation and extract region-specific information via the EBRAINS Knowledge Graph. Moving beyond the graphical interface of siibra-explorer, the session will proceed with siibra-python. Participants will be guided through coding exercises demonstrating how to fetch brain region maps, access the BigBrain dataset, and extract multimodal regional features.

In addition to the analog neural network core, BrainScaleS-2 features two embedded general-purpose processors, called PPUs (Plasticity Processing Units). They can be used for experiment control and alterations of configurations during the run-time of an experiment. This tutorial shows an example of using this feature by showcasing synaptic plasticity.

Hands on tutorial BrainScaleS 2 introduction

Introduction to the BrainScales Hardware and System and how to execute simple experiments.

A tutorial on how to anchoring partial volumetric data to BigBrain using VoluBA
HIBALL Winter School 2021 – BigBrain data and tools, Feb 4, 2021

Sebastian Bludau, Timo Dickscheid, Institute of Neuroscience and Medicine, Structural and functional organisation of the brain (INM-1), Forschungszentrum Jülich, Germany

VoluBA ("Volumetric Brain Anchoring") is an online service which allows to upload a high-resolution volume of interest (VOI) to perform interactive anchoring to the BigBrain, without the need of downloading the BigBrain volume to a local machine. VoluBA provides fast and intuitive manipulation of the 3D position, orientation, and scale of the VOI. Furthermore, it allows to precisely enter pairs of corresponding 3D landmarks and use them to refine the alignment by 3D affine parameters. In addition, a plugin is available for subsequent nonlinear adjustment of cortical VOIs, which exploits equivolumetric volumetric depth as a constraint in case that a segmentation of the gray matter is available. This tutorial provides a hands-on introduction to VoluBA, using a real world example dataset.

The QUINT workflow takes you through a series of steps developed to enable researchers to quantify and analyse labelled features in a series of histological images of rodent brain sections within a known atlas space. The QuickNII and VisuAlign software are used for precise image registration; ilastik allows efficient identification of labelled objects; finally, the Nutil tool enables quantification of features per atlas region. This combination of steps facilitates semi-automated quantification, eliminating the need for more time-consuming methods, such as stereological analysis with manual delineation of brain regions
More on https://ebrains.eu/service/quint/