Peering through Behavior into the Brain
Frequency
Only this year
Summary
This class will focus on new experimental approaches, quantitative models, computational frameworks, and advanced algorithms to decode neural activity from behavioral data.
Content
Students will engage in discussions on experimental paradigms, machine learning, dynamical systems theory, and statistical modeling, with the goal of advancing our understanding of the brain-behavior relationship through next-generation experimental and theoretical tools.
Lecturers:
Misha Ahrens (Janelia)
Georges Debrégeas (CNRS)
Wulfram Gerstner (EPFL)
Alice Gross (PhD student, EFPL)
Konrad Koerding (University of Pennsylvania)
Johannes Larsch (University of Lausanne)
Hang Lu (Georgia Tech)
Marcelo Mattar (NYU)
Remi Monasson (ENS)
Gonzalo Polavieja (Champalimaud Foundation)
Aravi Samuel (Harvard University)
Mei Zhen (University of Toronto)
Note
Organizers: Sahand Rahi and Johanni Brea
Dates: 8.-12. June 2026
Keywords
neurophysics, behavioral analysis, predictive modeling, machine learning, dynamical systems theory
Dans les plans d'études
- Nombre de places: 20
- Forme de l'examen: Rapport de TP (session libre)
- Matière examinée: Peering through Behavior into the Brain
- Cours: 12 Heure(s)
- Exercices: 12 Heure(s)
- Type: optionnel