Jump to Syllabus

Current announcements

Nothing a the moment.

Syllabus

Chapter 1: Behavioral experiments and animal welfare

Chapter 2: Multi-modal and high-throughput behavioral phenotyping

Chapter 3: Assessing animal behavior with machine learning

Chapter 4: Deciphering animal behavior and neuronal activity in latent space

Acknowledgements

In order to keep the lecture material open and free, the use of copyright-protected material has been avoided. Instead, freely accessible YouTube movies are embedded and quoted. The organizer of this lecture is not the author of these videos and is not responsible for their content. The organizer uses the embedded sources according to fair principles for educational purpose.

The organizer of this lecture is not affiliated with the authors of the content in the provided external links, and bears no responsibility for their content. These links are used solely for educational purposes.

Past courses

• -/-

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).

Jump to Syllabus

Current announcements

Nothing a the moment.

Course requirements

• Please, bring a laptop.
• Please, install conda before the course starts. Follow Step 1 in this installation guide.
• Please, download the course material, the links are provided in the installation guide.
• You can also bring your own images you have taken in the lab.

Syllabus

Tutorial 1: Napari installation guide

Tutorial 2: Basic handling of Napari

Tutorial 3: Reslicing stacks in Napari

Tutorial 4: Cropping images in Napari

Tutorial 5: Scale bars and adjusting image scaling

Tutorial 6: The Napari Assistant

Tutorial 7: Image denoising and background subtraction in Napari

Tutorial 8: Bleach correction in Napari

Tutorial 9: Spectral unmixing in Napari

Tutorial 10: Image registration in Napari

Tutorial 11: Image segmentation and feature extraction

Tutorial 12: Advanced cell segmentation with Cellpose

Tutorial 13: Segmenting densly packed cells with StarDist

Tutorial 14: Colocalizing cells

Tutorial 15: Tracking cell migration

Tutorial 16: Filament tracing

Tutorial 17: Data exploration, dimensionality reduction and clustering with Napari

Acknowledgements

Image credits

Exercises

Unless otherwise stated, please perform the examples shown in each tutorial. Use the image file(s) shown in the example or any other file provided in GitHub data folder. You can also use your own images taken in the lab or an appropriate sample image from the Cell Image Libraryꜛ. Also try to combine the techniques you have already learned in the previous tutorials (if appropriate). Each tutorial including reading time and exercise takes around 15-30 minutes.

Past courses

• -/-

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).

Jump to Syllabus

Current announcements

Nothing a the moment.

Course requirements

• a laptop or desktop computer (no specific requirements except an interntet conncetion) a computer will be provided on-site
• a working Fiji ꜛ installation; before the course starts, please make sure, that Fiji is working on your device (we can not provide installation or technical assistance during the course); find further information about Fiji on this website ꜛ
• a working spreadsheet tool (e.g., LibreOffice ꜛ, Google Sheets ꜛ, Excel ꜛ, Office 365 ꜛ, or Numbers ꜛ)
• please, download at least one of the two data sets provided in this Nextcloud folder ꜛ

Syllabus

Manual Ca²⁺ signal extraction with Fiji and a spreadsheet tool

Past courses

• 2021, August: 5th BIGS Summer School
• 2020, November: BIGS lecture (BIGS module 28)

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).

Jump to Syllabus

Current announcements

Nothing at the moment.

Course requirements

• basic Python programming skills, e.g., presented in the Python: Basics for Data Scientists course
• a laptop or desktop computer (no specific requirements except an internet connection) with a working Anaconda ꜛ installation
• please download in advance the course material from the course’s GitHub repository:
  1. on the GitHub repository page, click on the green “Code” button and choose “Download Zip” (example)
  2. extract the Zip package and move the unpacked folder to your desired location on your hard drive (e.g., create a course folder in your documents folder)
• during the course, please visit this website to stay up to date (see Current announcements section).

Important note: Before the course starts, please make sure, that Anaconda is working on your device. We can not provide installation or technical assistance during the course.

Trouble shootings: If you have problems with your computer and/or Anaconda, you can use an online Python compiler, e.g., Google Colab ꜛ. Please, ensure before the beginning of the course, that you can access the online compiler of your choice (e.g., create a Google account) and that you know how to operate it (again, during the course we can not provide installation or technical assistance).

Syllabus

Tutorial 1: Statistical data analysis with Pandas and Pingouin (extended)

Tutorial 2: Basic time series analysis

Tutorial 3: Python Data I/O

Tutorial 4: Analyzing patch clamp recordings

Tutorial 5: Using Fourier transform for time series decomposition

Tutorial 6: Improving matplotlib plots

Further Readings

Info: The chapters of this course are also available as Jupyter notebooks on , which can additionally be opened on

Past courses

• 2021, March: DZNE Workshop series (2 days)
• 2020-2021: Lab internal course series (weekly, closed)
• 2020, October: DZNE Workshop series (2 days)
• 2020, May: DZNE Workshop series (2 days)

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).

Jump to Syllabus

Current announcements

Course requirements

• a laptop or desktop computer (no specific requirements except an internet connection) with a working Anaconda ꜛ installation
• please download in advance the course material from the course’s GitHub repository:
  1. on the GitHub repository page, click on the green “Code” button and choose “Download Zip” (example)
  2. extract the Zip package and move the unpacked folder to your desired location on your hard drive (e.g., create a course folder in your documents folder)
• during the course, please visit this website to stay up to date (see Current announcements section).

Important note: Before the course starts, please make sure, that Anaconda is working on your device. We can not provide installation or technical assistance during the course.

Trouble shootings: If you have problems with your computer and/or Anaconda, you can use an online Python compiler, e.g., Google Colab ꜛ. Please, ensure before the beginning of the course, that you can access the online compiler of your choice (e.g., create a Google account) and that you know how to operate it (again, during the course we can not provide installation or technical assistance).

Syllabus

Chapter 1: Scientific programming languages

Chapter 2: Getting started with Anaconda and Spyder

Chapter 3: Jupyter Notebooks

Chapter 4: Variables

Chapter 5: Formatted printing

Chapter 6: Deep vs. shallow copy

Chapter 7: for-loops

Chapter 8: if-conditions

Chapter 9: Function definitions

Chapter 10: NumPy - Our data container

Chapter 11: Data visualization with Matplotlib

Chapter 12: Reading data with Pandas

Chapter 13: Statistical Analysis with Pingouin

Further Readings

Voluntary homework: After the first part of this course, i.e., after Chapter 9, feel free to solve this voluntary homework.

Info: Chapters 4 - 13 are available as Jupyter notebooks on , which can also be opened on

Follow-up

Don’t miss the Python Course: Neuro-Practical course, where you can apply your newly learned programming skills.

Past courses

• 2023, March: DZNE Workshop series (2.5 days)
• 2022, September: DZNE Workshop series (2.5 days)
• 2022, January: DZNE Workshop series (2.5 days)
• 2021, September: DZNE Workshop series (2 days)
• 2021, March: DZNE Workshop series (2 days)
• 2020-2021: Lab internal course series (weekly, closed)
• 2020, October: DZNE Workshop series (2 days)
• 2020, May: DZNE Workshop series (2 days)

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).

Jump to Syllabus

Current announcements

Nothing a the moment.

Course requirements

• desktop computers with a valid MATLAB licenses will be provided on site.
• please download the workshop material from the corresponding GitHub repository.

Syllabus

Script ꜛ

Past courses

• 2019, Ocotber: 4th DZNE Doctoral Retreat in Dresden, Germany (1 day)

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).

Past courses

• 2015/2016 (Winter Term): tutor in the lecture Geophysik des Erdkörpers (geodynamics), University of Cologne, Germany
• 2015 (Summer Term): tutor in the lecture Space Physics, University of Cologne, Germany
• 2013/2014 (Winter Term): tutor in the lectur Prognostische Modellierung (prognostic modeling)
• 2012/2013 (Winter Term): tutor in the lecture Geophysikalische Fluiddynamik (fluid dynamics), University of Cologne, Germany
• 2012, 2013, 2014, 2015 and 2016 (Summer Term): tutor in the lecture Einführung Geophysik (introduction into Geophysics), University of Cologne, Germany