General software skills for systems neuroscience#

Overview#

Held over seven days, this course will provide researchers with some basic computational skills for systems neuroscience research. This will include general programming, software development best practices and data analysis using leading open-source software tools. This course will also include some general microscopy lectures.

Schedule#

Date

Time

Event

Location

Monday, September 30th

13:00-17:30

Introduction to Python (1)

SWC Brasserie Seminar Room

Tuesday, October 1st

09:45-10:45

Data management and sharing (1)

SWC Ground Floor Lecture Theatre

15:00-17:30

Data management and sharing (2)

SWC Ground Floor Lecture Theatre

Wednesday, October 2nd

10:00-12:30

Introduction to Python (2)

GCNU Seminar Room

13:30-17:30

Version control and software development best practices

GCNU Seminar Room

Thursday, October 3rd

10:00-13:00

Video-based analysis of animal behaviour (1)

SWC Brasserie Seminar Room

14:00-17:30

Video-based analysis of animal behaviour (2)

SWC Brasserie Seminar Room

Friday, October 4th

14:00-17:30

Linux and high-performance computing

SWC Ground Floor Lecture Theatre

Monday, October 7th

11:00-12:00

General microscopy: basics

SWC Brasserie Seminar Room

13:00-14:30

General microscopy: applications

SWC Brasserie Seminar Room

14:30-18:00

Histology analysis (1)

SWC Brasserie Seminar Room

Tuesday, October 8th

10:00-11:00

Histology analysis (2)

SWC Ground Floor Lecture Theatre

14:00-17:30

Histology analysis (3)

SWC Brasserie Seminar Room

Introduction to Python#

The aim of the course is to introduce basic concepts of programming in Python, and establish a community of Python users. It is a hands-on course, in which we will guide you through the process of writing your first Python script and teach you some best practices.

The course will run over two days, 2.5-3h per day with a break in between.

Full details can be found on the course webpage

Instructors: Igor Tatarnikov, Sofía Miñano

Data Management and Sharing#

This course will introduce best-practices in neuroscience data management and cover recent standardisation initiatives. The primary take-away from this course will be to leave with a strong schema in mind for clean organisation of experimental data.

Full details can be found on the course webpage

Version control and software development best practices#

The aims of the course are

  • to learn how to keep track of changes to your code with Git and GitHub,

  • to learn how to structure your code well

  • and to get an initial idea of how work collaboratively on a code base, and how to document and test your code.

Git and Github slides for 2023/2024 course

Site for software development best practice

Instructors: Stephen Lenzi, Laura Porta, Alessandro Felder

Video-based analysis of animal behaviour#

This course will introduce the theory and practice of tracking animals in videos to quantify their behaviour. Participants will get to train pose estimation models with SLEAP, analyse pose tracks with movement, and extract behavioural syllables with keypoint-moseq.

Important

Please install the necessary software and download the required data ahead of the course. Full details can be found on the course webpage. If you encounter any issues, please contact Niko Sirmpilatze.

Instructors: Niko Sirmpilatze, Chang Huan Lo, Sofía Miñano

Linux and high-performance computing#

This course will introduce some basic principles of using Linux, and high-performance computing in general. Most of the course will be centered around learning to run a specific workflow (pose estimation) on the SWC HPC system.

Full details can be found on the course webpage.

Instructors: Niko Sirmpilatze, Igor Tatarnikov, Adam Tyson

General microscopy#

This course will introduce some basic concepts about microscopy (optics, fluroescence etc.) to help better understand the following course on histology analysis.

Instructors: Rob Campbell, Adam Tyson, Alessandro Felder, Igor Tatarnikov

Histology analysis#

Full details can be found on the course webpage.