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Organizing recurrent network dynamics by task-computation to enable continual learning
Here, we develop a novel learning rule designed to minimize interference between sequentially learned tasks in recurrent networks.
Learning interpretable continuous-time models of latent stochastic dynamical systems
We develop an approach to learn an interpretable semi-parametric model of a latent continuous-time stochastic dynamical system, assuming noisy high-dimensional outputs sampled at uneven times.
Temporal alignment and latent Gaussian process factor inference in population spike trains
We introduce a novel scalable approach to identifying common latent structure in neural population spike-trains, which allows for variability both in the trajectory and in the rate of progression of the underlying computation.
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