Inferring Neural Dynamics with Variational Schrödinger Bridges

Neural population activity evolves within low-dimensional dynamical spaces, yet recovering these dynamics from electrophysiological recordings remains difficult. This is particularly challenging for spontaneous neural activity, where there are no repeated trials or known inputs, and where the recorded network continuously transitions between internally generated and externally driven states. Here, we introduce NeuBridge, a variational extension of Schrödinger bridges to infer stochastic latent dynamics from neural population activity. We apply NeuBridge to large-scale Neuropixels recordings from the brainstem of sleeping mice, where neural activity evolves spontaneously across sleep-wake states and is occasionally perturbed by optogenetic stimulation of upstream populations. NeuBridge reconstructs firing rates more accurately than existing latent-dynamics methods and recovers latent dynamical structure across mice. Importantly, NeuBridge also estimates the posterior control cost required to move neural activity away from its intrinsic dynamics. Without being given the timing of laser stimulation, NeuBridge detects a clear increase in control cost during optogenetic perturbations. These results show that NeuBridge can recover low-dimensional stochastic dynamics from spontaneous neural activity and can quantify the external control effort required to explain perturbation-driven deviations from intrinsic dynamics.