Topic: reaching tasks

Large scale neural recordings are typically found to embed lower-dimensional structure that reflects behaviour. Empirically, the best models of such low-dimensional structure -- by several statistical measures -- tend to be those that describe neural recordings via a latent dynamical system. Importantly, as recordings are typically made in one or a subset of areas within the brain, the dynamics that best capture the data cannot in general be expected to be fully autonomous, but may instead be driven by unobserved inputs. Learning the parameters of a probabilistic dynamical system whilst simult

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