Assessing Animal Behavior
Current announcements
Nothing at 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 (incl. discussion)
Chapter 5: Hands-on: DeepLabCut
Learning outcomes
After completing this lecture series, students will be able to:
- Explain the scientific importance of animal behavior experiments in neuroscience, including how behavioral studies contribute to understanding brain function, neural circuits, and the pathophysiology of neurological disorders.
- Summarize the ethical guidelines governing animal research, with emphasis on the Three Rs principle (Replacement, Reduction, Refinement), and discuss national and EU-level regulations for animal welfare in neuroscience.
- Identify and differentiate common animal models (e.g., mice, zebrafish, fruit flies) used in behavioral neuroscience, and discuss their specific advantages and limitations.
- Describe multi-modal and high-throughput behavioral phenotyping approaches, including the integration of multiple sensors and recording modalities for comprehensive behavioral analysis.
- Discuss and evaluate automated tracking systems and machine vision approaches for behavioral analysis, including their ability to increase data throughput, reduce bias, and improve reproducibility.
- Explain the application of machine learning (ML) methods in behavioral data analysis, including supervised, unsupervised, and reinforcement learning, and their roles in automated classification and detection of behavioral patterns.
- Apply knowledge of ML-based tools, such as DeepLabCut and VAME, for markerless pose estimation, behavior segmentation, and automated analysis of complex behavioral datasets.
- Describe the challenges and strategies for analyzing high-dimensional behavioral and neural data, including the use of dimensionality reduction techniques (e.g., PCA, t-SNE, autoencoders, CEBRA) and their interpretational limitations.
- Perform hands-on behavioral data analysis, including:
- Installing and configuring DeepLabCut
- Creating and managing new projects
- Extracting and labeling video frames
- Training and evaluating neural networks for pose estimation
- Analyzing videos and interpreting results.
- Critically assess the strengths, limitations, and ethical implications of modern behavioral phenotyping and analysis approaches, particularly regarding animal welfare and scientific reproducibility.
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
- Neuronal Circuit Dysfunction of CNS diseases (WPM31), July 7, 2025
- Neuronal Circuit Dysfunction of CNS diseases (WPM31), June 24, 2024
- Neuronal Circuit Dysfunction of CNS diseases (WPM31), July 5, 2023
This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).
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