Understanding reward-guided learning using large-scale datasets
Jul 9, 2025
Kim Stachenfeld
Bridging machine learning and mechanistic modelling
Mathematical Modeling, Machine Learning, Applied Mathematics and seminar by Prof Jakob Macke, University of Tubingen
Hosted by NeuroAI UCL
Wednesday 13:00–14:10 London (GMT+0)
Ended
Online
No abstract or recording was published for this talk.
More from NeuroAI UCL
All talksNeurobiological constraints on learning: bug or feature?
Jun 11, 2025Cian O’DonellUnderstanding reward-guided learning using large-scale datasets
May 14, 2025Kim StachenfeldRelating circuit dynamics to computation: robustness and dimension-specific computation in cortical dynamics
Apr 23, 2025Shaul Druckmann
More with Jakob Macke
All their workAdaptive probabilistic regression for real-time motor excitability state prediction from human EEG
ePoster · Sep 29, 2024Bernstein Conference 2024Bridging biophysics and computation with differentiable simulation
ePoster · Sep 29, 2024Bernstein Conference 2024Building mechanistic models of neural computations with simulation-based machine learning
ePoster · Sep 29, 2024Bernstein Conference 2024Connectome and task predict neural activity across the fly visual system
ePoster · Sep 29, 2024Bernstein Conference 2024
Upcoming in Mathematical Modeling
All seminarsBeyond Modeling: How ML Is Reshaping Operations Research for Dynamic Decision Making
Nov 23, 2026Shadi Sharif Azadeh
Related seminars
Learning mechanistic models that link cells, circuits, and computations
Video · 45 min · Dec 17, 2025Jakob Macke, Tubingen UniversityCan machine learning learn new physics, or do we need to put it in by hand?"\
Video · 3 h 30 min · Jun 4, 2020Workshop, Multiple Speakers, Emory UniversityUnderstanding machine learning via exactly solvable statistical physics models
Video · 1 h 13 min · Jun 24, 2020Lenka Zdeborová, CNRS & CEA Saclay