  Neural population activity is theorized to reflect an underlying dynamical
structure. This structure can be accurately captured using state space models
with explicit dynamics, such as those based on recurrent neural networks
(RNNs). However, using recurrence to explicitly model dynamics necessitates
sequential processing of data, slowing real-time applications such as
brain-computer interfaces. Here we introduce the Neural Data Transformer (NDT),
a non-recurrent alternative. We test the NDT's ability to capture autonomous
dynamical systems by applying it to synthetic datasets with known dynamics and
data from monkey motor cortex during a reaching task well-modeled by RNNs. The
NDT models these datasets as well as state-of-the-art recurrent models.
Further, its non-recurrence enables 3.9ms inference, well within the loop time
of real-time applications and more than 6 times faster than recurrent baselines
on the monkey reaching dataset. These results suggest that an explicit dynamics
model is not necessary to model autonomous neural population dynamics. Code:
https://github.com/snel-repo/neural-data-transformers
