Interpretable statistical representations of neural population dynamics and geometry

arXiv:2304.03376v3 Announce Type: replace
Abstract: The dynamics of neuron populations during many behavioural tasks evolve on low-dimensional manifolds. However, it remains challenging to discover latent representations from neural recordings that are interpretable and consistently decodable across individuals and conditions without explicitly relying on behavioural information. Here, we introduce MARBLE, a fully unsupervised geometric deep learning framework for the data-driven representation of non-linear dynamics based on statistical distributions of local dynamical features. Using both in silico examples from non-linear dynamical systems …

abstract arxiv cs.lg decodable dynamics geometry however information low math.ds neuron population q-bio.nc q-bio.qm statistical tasks type