  A central challenge in neuroscience is understanding how neural system
implements computation through its dynamics. We propose a nonlinear time series
model aimed at characterizing interpretable dynamics from neural trajectories.
Our model assumes low-dimensional continuous dynamics in a finite volume. It
incorporates a prior assumption about globally contractional dynamics to avoid
overly enthusiastic extrapolation outside of the support of observed
trajectories. We show that our model can recover qualitative features of the
phase portrait such as attractors, slow points, and bifurcations, while also
producing reliable long-term future predictions in a variety of dynamical
models and in real neural data.
