  Neuroscience is experiencing a data revolution in which many hundreds or
thousands of neurons are recorded simultaneously. Currently, there is little
consensus on how such data should be analyzed. Here we introduce LFADS (Latent
Factor Analysis via Dynamical Systems), a method to infer latent dynamics from
simultaneously recorded, single-trial, high-dimensional neural spiking data.
LFADS is a sequential model based on a variational auto-encoder. By making a
dynamical systems hypothesis regarding the generation of the observed data,
LFADS reduces observed spiking to a set of low-dimensional temporal factors,
per-trial initial conditions, and inferred inputs. We compare LFADS to existing
methods on synthetic data and show that it significantly out-performs them in
inferring neural firing rates and latent dynamics.
