Inferring stochastic low-rank recurrent neural networks from neural data Matthijs Pals 1,2 A Erdem Sa gtekin 1,2,3 Felix Pei 1,2 Manuel Gloeckler

However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods.