The geometry & dynamics of learning— 2024 —

Recent advances in neural recording have revolutionised our ability to record from populations of neurons over time and at higher resolution. These advances stand to challenge existing knowledge about the dynamical nature of learning in neural systems, which, in turn, may reveal how representations underlying complex behaviours develop over time in neural architectures. Simultaneously, progress in artificial neural networks has revealed the geometric properties of emergent neural representations, along with analytic insights into the learning dynamics that shape these representations. Despite such massive strides in both fields, we still lack a common understanding of how distinct learning strategies in the brain can shape the observed neural population structures. The present workshop aims to bridge that gap by fostering discussion between experimentalists working on changes in neural activity in the brain over learning and theorists working on learning in neural network models in order to better understand learning at the level of populations of neurons.