Two (+ one) weeks of learning, coding, and networking
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.