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Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
Yuan Zhao · Il Memming Park

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #97

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.

Author Information

Yuan Zhao (Stony Brook University)
Il Memming Park (Stony Brook University)

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