Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a revolution in which simultaneous recording of many thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data, but deeper understanding requires studying single-trial phenomena, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system to infer the dynamics underlying observed spiking activity and to extract ‘de-noised’ single-trial firing rates. When applied to a variety of monkey and human motor cortical datasets, LFADS predicts observed behavioral variables with unprecedented accuracy, extracts precise estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.