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The simulation of the virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved results in clinics. The fundamentals of epilepsy are explained. On this basis, the concept of epilepsy simulation is developed. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.

Difficulty level: Beginner
Duration: 1:28:53
Speaker: : Julie Courtiol

Félix-Antoine Fortin from Calcul Québec gives an introduction to high-performance computing with the Compute Canada network, first providing an overview of use cases for HPC and then a hand-on tutorial.  Though some examples might seem specific to the Calcul Québec, all computing clusters in the Compute Canada network share the same software modules and environments.

 

The lesson was given in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 02:49:34
Speaker: :

The Canadian Open Neuroscience Platform (CONP) Portal is a web interface that facilitates open science for the neuroscience community by simplifying global access to and sharing of datasets and tools. The Portal internalizes the typical cycle of a research project, beginning with data acquisition, followed by data processing with published tools, and ultimately the publication of results with a link to the original dataset.

 

In this video, Samir Das and Tristan Glatard give a short overview of the main features of the CONP Portal.

Difficulty level: Beginner
Duration: 14:03
Speaker: :

Shawn Brown presents an overview of CBRAIN, a web-based platform that allows neuroscientists to perform computationally intensive data analyses by connecting them to high-performance-computing facilities across Canada and around the world.

 

This talk was given in the context of a Ludmer Centre event in 2019.

 

 

Difficulty level: Beginner
Duration: 56:07
Speaker: :

Estefany Suárez provides a conceptual overview of the rudiments of machine learning, including its bases in traditional statistics and the types of questions it might be applied to.

 

The lesson was presented in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 01:22:18
Speaker: :

Jake Vogel gives a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.

 

The lesson was presented in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 02:13:53
Speaker: :

Gael Varoquaux presents some advanced machine learning algorithms for neuroimaging, while addressing some real-world considerations related to data size and type.

 

The lesson was presented in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 01:17:14
Speaker: :

Dr. Guangyu Robert Yang describes how Recurrent Neural Networks (RNNs) trained with machine learning techniques on cognitive tasks have become a widely accepted tool for neuroscientists. In comparison to traditional computational models in neuroscience, RNNs can offer substantial advantages at explaining complex behavior and neural activity patterns. Their use allows rapid generation of mechanistic hypotheses for cognitive computations. RNNs further provide a natural way to flexibly combine bottom-up biological knowledge with top-down computational goals into network models. However, early works of this approach are faced with fundamental challenges. In this talk, Dr. Guangyu Robert Yang discusses some of these challenges, and several recent steps that we took to partly address them and to build next-generation RNN models for cognitive neuroscience.​

Difficulty level: Beginner
Duration: 00:51:12
Speaker: :

In this presentation by the OHBM OpenScienceSIG, Tom Shaw and Steffen Bollmann cover how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers. They demonstrate how to build docker containers from scratch, using Neurodocker, and cover how to use containers on an HPC with singularity.

 

 

Difficulty level: Beginner
Duration: 01:21:59

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

Difficulty level: Beginner
Duration: 37:51

Introduction to neurons, synaptic transmission, and ion channels.

Difficulty level: Beginner
Duration: 46:07

Introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.

Difficulty level: Beginner
Duration: 40:32

This lecture covers: integrating information within a network, modulating and controlling networks, functions and dysfunctions of hippocampal networks, and the integrative network controlling sleep and arousal.

Difficulty level: Beginner
Duration: 47:05

This lecture focuses on the comprehension of nociception and pain sensation. It highlights how the somatosensory system and different molecular partners are involved in nociception and how nociception and pain sensation are studied in rodents and humans and the development of pain therapy.

Difficulty level: Beginner
Duration: 28:09
Speaker: : Serena Quarta

How genetics can contribute to our understanding of psychiatric phenotypes.

Difficulty level: Beginner
Duration: 55:15
Speaker: : Sven Cichon

Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. This lecture provides an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.

Difficulty level: Beginner
Duration: 1:00:39
Speaker: : Sharon Crook

Introduction to reproducible research. The lecture provides an overview of the core skills and practical solutions required to practice reproducible research. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 1:25:17
Speaker: : Fernando Perez

Brought to you by the Canadian Association of Research Libraries.

 

Keeping data and research materials organized across all phases of the research process is always a challenging process. To help the research community address these challenges, the Center for Open Science developed the Open Science Framework (OSF), a research tool that supports collaboration, data management, and transparency throughout the research lifecycle. The OSF provides avenues for researchers to design a study; collect, analyze, and store data; manage collaborators; and publish research materials. In this webinar, attendees will learn about the many features of the OSF and develop strategies for using the tool within the context of their own research projects. The discussion will be framed around how to best utilize the OSF while also implementing data management and open science best practices.

 

Speakers Kevin Read, MLIS, MAS is a health sciences librarian at the University of Saskatchewan. He has been providing data services in health sciences libraries for the past 8 years in both Canada and the U.S. He is the current Chair of the Portage Network’s Data Discovery Expert Group, and is in the process of conducting research on how Canadian-funded researchers describe and share their data.

Difficulty level: Beginner
Duration:
Speaker: :

The FOSTER portal has produced a number of guides to help implement Open Science practices in daily workflows, including The Open Science Training Handbook.  It provides many basic definitions, concepts, and principles that are key components of open science, as well as general guidance for developing and implementing these practices in one's own research environments.

 

Topics include:

  • Open Concepts and Principles
  • Open Research Data and Materials
  • Open Research Software and Open Source
  • Reproducible Research and Data Analysis
  • Open Access to Published Research Results
  • Open Licensing and File Formats
  • Collaborative Platforms
  • Open Peer Review, Metrics and Evaluation
  • Open Science Policies
  • Citizen Science
  • Open Educational Resources
  • Open Advocacy
Difficulty level: Beginner
Duration:
Speaker: :