Workshop (3 hours), Sophia Pieschnik, Signý Benediktsdóttir, Maya Kobchenko, Archana Golla (University of Oslo)

The EBRAINS research infrastructure includes a database of neuroscience data, computational models and software. In this workshop the EBRAINS Data Curation Team will introduce the EBRAINS data sharing workflow from initiating curation to publishing data. They will explain the steps involved and give practical guidance on how data and metadata should be prepared, how different computational resources can be utilized, and how data can be found and used. The workshop will include hands-on sessions allowing participants to experience different workflow steps working with example data sets. The hands-on sessions will include modules adapted to the participants’ competence and needs. The Data Curation Team will also be available for drop in consulting of participants data sets.

Demonstration (60 min): Maja Puchades (University of Oslo, Norway)

The QUINT workflow is an analysis solution for 2D rodent brain microscopy data, enabling brain-wide mapping and regional quantification using a reference brain atlas. It combines the use of several software with graphical user interfaces; hence no coding ability are required. The QUINT workflow takes brain section image series as input, and generates counts of labelled objects, area fraction per atlas-region, and coordinates for visualising objects in 3D atlas space. 

Demonstration (45 min): Trygve Leergaard & Maja Puchades (University of Oslo, Norway)

Open access three-dimensional brain atlases provide new opportunities for navigating complex neuroanatomy, defining regions-of-interest, and visualizing different parts of the brain. In this session Maja Puchades and Trygve Leergaard will give some practical examples of how brain atlases and software tools available via EBRAINS can be used for stereotaxic navigation in the mouse and rat brain, to visualize and analyse data, and for making “eye candy” visualizations for illustrations of brain anatomy. Participants are encouraged to bring computers for hands-on exploration of possibilities.

Duration: 2 hours (+plus optional siibra-python course)

Instructor: Sebastian Bludau, PhD     Ahmet Nihat Simsek,PhD             Jülich Research Centre

About: 

• siibra-explorer 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

• siibra-python is a Python client to a brain atlas framework that integrates brain parcellations and reference spaces at different spatial scales, and connects them with a broad range of multimodal data features. It aims to facilitate programmatic and reproducible incorporation of brain parcellations and brain region features from different sources into neuroscience workflows.

• Voluba is a browser-based tool for interactively aligning volumes of interest to 3D reference templates. Voluba currently supports the BigBrain model, Waxholm rat template, and Allen mouse template as reference spaces. Voluba is compatible with siibra-explorer, so you can directly inspect aligned data superimposed with brain region maps and other datasets

Format: This hands-on session will offer participants an immersive opportunity to explore the advanced tools and techniques for data analysis and visualization. Participants will be introduced to the siibra tool suite with highlights on its features and benefits. Participants will learn to access 3D reference templates and maps, including anatomical, and connectivity atlases. Participants 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 such as cortical thicknesses, cell and neurotransmitter densities, gene expressions, and connectivity data. In addition, participants will be introduced to Voluba.  After completing the training, participants will have a first insight of the features of siibra-explorer,  siibra-python, and Voluba to enhance their ability to explore brain atlases and perform advanced neuroimaging analyses.

Requirements: A laptop with an up-to-date web browser (Chrome or Firefox is recommended) is required for the hands-on examples. All examples will be run on pre-built Jupyter notebooks, which will be provided for downloading. Please register for an EBRAINS account in advance.

Target audience:

This beginner-friendly course is aimed at researchers interested in exploring human brain atlases using siibra-explorer and Voluba. No programming experience is required. Participants will interact with both tools through intuitive, web-based interfaces. Toward the end of the course, we will review and explain working siibra-python code examples. While no coding is required, basic understanding of the examples will be supported and encouraged.

An optional follow-up session will be offered for participants interested in hands-on coding with siibra-python. This part of the session will focus on creating our own workflow and some more advanced functionalities of siibra-python. For this, a working local Python setup with siibra-python installed (pip install siibra) and basic coding experience in Python are required.

Duration: 6 hours

Instructor: Michael Denker, PhD                     Jülich Research Centre

About:

• Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data (such as OpenElectrophy, NeuroTools, G-node, Helmholtz, PyNN) by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to representation of data, with no functions for data analysis or visualization

• Elephant, the Python library Electrophysiology Analysis Toolkit, provides tools for analysing neuronal activity data, such as spike trains, local field potentials and intracellular data. In addition to providing a platform for sharing analysis codes from different laboratories, Elephant provides a consistent and homogeneous framework for data analysis built on a modular foundation. The underlying data model is the Neo library. This framework easily captures a wide range of neuronal data types and methods, including dozens of file formats and network simulation tools. A common data description, as the Neo library provides, is essential for developing interoperable analysis workflows

Format: This hands-on tutorial delves into challenges in the reproducibility of neuroscience workflows dealing with classical electrophysiological activity data on the cellular scale, such as spiking data or local field potentials, from experiment or simulation. The training will cover the complete cycle from generating structured and consistent data and metadata, accessing the data, pre-processing, setting up analysis workflows, up to the tracking of the provenance of the analysis results. In this context, the e-infrastructure services of EBRAINS offer a mature data, software and compute services ecosystem with community-driven tools developed in the framework of the Human Brain Project. In the first part of the workshop, participants will be trained in the use of tools covering the following topics: reading and manipulating electrophysiology data in Python using Neo, analysis of such data using Elephant, best practices for integrating metadata into your workflow to aid the analysis process, and structuring analysis results for sharing, e.g., using the nix data format. At the end of the workshop, time is dedicated for participants to explore the tools on their own data and particular personal interests. 

Requirements: Basic Python knowledge

Target audience: Neuroscientists analyzing electrophysiology data on the cellular scales (spikes, LFP); computational neuroscientists performing simulations on a comparable scale (spiking neural network simulations)

Duration: 1 full day

Instructor: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich

About:

NEST is an established, open-source simulator for spiking neuronal networks, which can capture a high degree of detail of biological network structures while retaining high performance and scalability from laptops to HPC [1]. This tutorial offers hands-on experience in building and simulating neuron, synapse, and network models. It introduces several tools and front-ends to implement modeling ideas most effectively. Participants do not have to install software as all tools are accessible via the cloud. 

Format: 

First, we look at NEST Desktop [2], a web-based graphical user interface (GUI), which allows the exploration of essential concepts in computational neuroscience without the need to learn a programming language. This advances both the quality and speed of teaching in computational neuroscience. To get acquainted with the GUI, we will create and analyze a balanced two-population network.

The tutorial will then turn to Jupyter (Python) notebooks where we will start by creating a spiking network. Here, we learn advanced steps to write code with NEST Simulation syntax. The scripting codes allow us  to explore sophisticated use cases with NEST simulations. I will let the audience pick one or few of the provided examples, e.g. large scale networks, networks of spatial neurons or using plasticity [3].

The last part is using NESTML to create custom neuron and synapse models for NEST Simulator. A functional plasticity rule will then be introduced into the balanced E/I network to implement a biologically realistic version of reinforcement learning. This will be done by formulating the learning model in the NESTML language syntax, and using the associated toolchain to generate code for NEST [4].

[1] https://nest-simulator.readthedocs.org/
[2] https://nest-desktop.readthedocs.org/
[3] https://nest-simulator.readthedocs.io/en/latest/examples/index.html
[4] https://nestml.readthedocs.org/

Requirements: Laptop with access to Internet. An account on EBRAINS would be optimal, otherwise I will create guest accounts for participants.

Target audience: Students and researchers who are interesting in computational neuroscience