Markovian analysis of Zebrafish swimming
Event details
| Date | 10.06.2026 |
| Hour | 14:30 › 15:30 |
| Speaker | Prof. Mattéo Dommanget-Kott <[email protected]> |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract :
In this interactive Python tutorial, we will learn how Markovian frameworks can be used to analyze and model spontaneous locomotion from behavioral time series. We will use zebrafish reorientation behavior as example.
Starting from discrete swim-bout sequences, we will introduce Markov chains as tools for quantifying transition structure, behavioral persistence, and temperature-dependent changes in exploratory strategies.
If time allows, we will then extend this approach to Hidden Markov Models, showing how latent behavioral states can be inferred directly from continuous kinematic observations.
In this interactive Python tutorial, we will learn how Markovian frameworks can be used to analyze and model spontaneous locomotion from behavioral time series. We will use zebrafish reorientation behavior as example.
Starting from discrete swim-bout sequences, we will introduce Markov chains as tools for quantifying transition structure, behavioral persistence, and temperature-dependent changes in exploratory strategies.
If time allows, we will then extend this approach to Hidden Markov Models, showing how latent behavioral states can be inferred directly from continuous kinematic observations.
Practical information
- Informed public
- Registration required
Organizer
- Prof. Sahand Jamal Rahi <[email protected]> Johanni Brea <[email protected]>
Contact
- Prof. Sahand Jamal Rahi <[email protected]> Johanni Brea <[email protected]>