ePoster · Computational Neuroscience
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Yenho Chen · Neuromatch 5
Wed, Sep 28, 2022
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 decomposed dynamical system model that represents com