Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics. (arXiv:2206.02972v1 [stat.ML])

Learning interpretable representations of neural dynamics at a population
level is a crucial first step to understanding how neural activity relates to
perception and behavior. Models of neural dynamics often focus on either
low-dimensional projections of neural activity, or on learning dynamical
systems that explicitly relate to the neural state over time. We discuss how
these two approaches are interrelated by considering dynamical systems as
representative of flows on a low-dimensional manifold. Building on this
concept, we propose a new …

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