This winter school brings together researchers from neuroscience, machine learning, information theory, and applied mathematics to study learning, computation, and representation in complex systems. Topics range from neural dynamics and synaptic plasticity to data-driven discovery of dynamical models, biologically inspired machine learning, information-theoretic approaches to causality, and experimental and data-analytic perspectives. The goal is to foster a shared understanding of how brains and machines learn, represent structure in the world, and give rise to coherent computation across scales.
- The program will contain lectures from invited speakers and researchers from Göttingen, hands-on tutorials, a hackathon, lab tours, a poster session, and networking activities.
- A Special session with a dedicated lecture on the Philosophy and Ethics of Artificial Intelligence.
- No registration fees and applications are open until Sep 1 2026.
- The Venue is the Max Planck Institute for Dynamics and Self-Organization Am Faßberg 17, 37077 Göttingen (View on map)
The program is organized around three main topic blocks:
Theory of emergence and computation
What does it mean for higher-level computation or emergence to exist in a system? This block introduces conceptual and mathematical frameworks for understanding emergent macrovariables, dynamical autonomy, compositionality, and information-theoretic approaches to computation and causality.
Learning in brains and machines
How are useful representations and world models learned at the system level? This block explores learning objectives, representation formation, neural world models, data-driven discovery of dynamical systems, and connections between biological and artificial learning.
Computation from local interactions
How do local mechanisms give rise to learning and computation? This block focuses on neural dynamics, synaptic plasticity, local learning rules, network mechanisms, and the ways in which microscopic interactions can support coherent computational behavior at larger scales.