Open Neurophysiology – analysis tools and datasets

Aim and content

This course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD Students from NorDoc member universities. All other participants must pay the course fee.

Anyone can apply for the course, but if you are not a PhD student at a Danish university, you will be placed on the waiting list until enrollment deadline.

This also applies to PhD students from NorDoc member universities. After the enrollment deadline, available seats will be allocated to applicants on the waiting list.

Learning objectives

A student who has met the objectives of the course will be able to:

1. Search for and explore open neuroscience datasets and tools
2. Use open analysis tools to analyze open electron microscopy data
3. Use open analysis tools to analyze behavioural data (DeepLabCut)
4. Use open analysis tools to explore and analyze open extracellular electrophysiology data
5. Explain “FAIR” principles in data and code sharing

Content

In recent years, an increasing number of datasets and analysis tools have become freely available. These come for national and international research initiatives, as well as individual labs and researchers. However, the majority of researchers are not aware of these resources, or they do not possess the skills to make full use of them. This five-day course will focus on open data and tools in neurophysiology.

Day 1:
On day one we will provide an overview of open science initiatives, with a focus on neuroscience data depositories and tools. We will discuss the idea behind open science and how it can potentially revolutionize neuroscience research, but also the challenges ahead. We will introduce the tools we will use over the course and explain the structure of the course. The day will conclude with a talk from Adrien Peyrache who has addressed interesting questions using open data and his team have been developing open analysis tools.

Day 2:
On day two we will focus on open microscopy data. We will provide an overview of initiatives with a focus on microscopy data from the Allen Brain Observatory Microns dataset. Through hands-on exercises, participants will learn how to explore the Micron dataset and get insights into the organization of the mouse cortex. The day will conclude with a talk from Hanieh Falahati on utilizing open electron microscopy datasets to address interesting biological questions.

Day 3:
On day three we will focus on open tools for analysis of behavior. We will provide a high-level overview of the currently available tools for conducting behavioral experiments and analyzing behavior. We will then focus on using open analysis tools to explore behaviour data in depth. Through a series of short exercises, participants will use basic programming to analyze coordinate tracking data from animal models. At the end of the day, Eric Yttri will present his work on developing B-SOiD, an open software for the study of movement behaviour.

Day 4:
On day four we will turn our attention to open extracellular neurophysiological data from the Allen Brain Observatory. This will involve a series of exercises using Allen’s Software Development Kit. The day will close with a talk by Steffen Schneider who has developed an open analysis toolkit for the joint analysis of behaviour and neural data.

Day 5:
On day five we will provide an overview of data and code sharing (e.g. “FAIR” principles). Moreover, students will present the group project they will have been working on the previous 3 days of the course. The course will conclude with a general discussion and feedback.

All materials and additional links for further reading and practice will be made available to students at the end of the course.

Participants

The course is aimed at Ph.D students who would like to learn how to access and analyze open datasets in their research. The course involves the use of analysis tools written in Python. While proficiency in any of these programming languages is not essential, participants with previous programming experience will be able to follow the material more easily. Prior to the course we will provide further information as well as material to help students take the most out of the course. The course is mainly relevant to PhD students specializing in neuroscience, but anyone interested in open science and open datasets can take part. Knowledge of calculus and linear algebra will be beneficial. Students should bring their laptops to follow tutorials and exercises. No other specific software is needed, except a Gmail account needed to access the Google Colab coding platform. Further instructions will be provided prior to the course.

Relevance to graduate programmes

The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:

Neuroscience

In Vivo Pharmacology and Experimental Animals

Language

English

Form

Lectures, group exercises, tutorials

Course directors

Hajime Hirase, Professor, Center for Translational Neuromedicine, hirase@sund.ku.dk

Peter Petersen, Assistant Professor, Department of Neuroscience, petersen.peter@sund.ku.dk

Antonios Asiminas, Postdoctoral Researcher, Center for Translational Neuromedicine, a.asiminas@sund.ku.dk

Teachers

Antonios Asiminas, Postdoctoral Researcher, Center for Translational Neuromedicine

Peter Petersen, Assistant Professor, Department of Neuroscience

Søren Grubb, Assistant Professor, Center for Translational Neuromedicine

Jared Cregg, Assistant Professor, Department of Neuroscience

External Speakers:

Adrien Peyrache, Assistant Professor, McGill University, Canada

Hanieh Falahati, Postdoctoral Fellow, Yale University, USA

Steffen Schneider, Group Leader, Helmholtz Munich, Germany

Eric Yttri, Associate Professor, Carnegie Mellon, USA

Dates

15 – 19 April 2024

Course location

Mærsk Tower, Room 7.15.149

Registration

Please register before 24 March 2024

Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment.

Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

Disclaimer:
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