Topic: dynamical embedding

Latent variable models are widely used in neuroscience to extract dynamical structure from high-dimensional neural activity, and provide valuable insights into neural computation. However, these models are typically inferred from a single recording session and exhibit limited generalization capabilities. This is primarily due to statistical heterogeneities in neural recordings across subjects and tasks, along with lack of inductive biases for disentangling shared and dataset-specific dynamical structure. In this work, we hypothesize that similar behavioral tasks admit a corresponding family of