Topic: Geometric Deep Learning

Computations in the brain and artificial neural networks can be understood as dynamical processes conformed by the activity of large neural populations. Yet revealing the structure of the underpinning latent dynamical processes from data and interpreting their relevance in computational tasks remains a fundamental challenge. Recent evidence suggests that task-relevant neural activity takes place on low-dimensional subspaces of the state space called neural manifolds. However, there is a lack of theoretical frameworks for the unsupervised representation of neural dynamics that are interpretable