Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.

ABSTRACT: High-dimensional imaging of neural activity, such as widefield calcium and functional ultrasound imaging, provide a rich source of information for understanding the relationship between brain activity and behavior. Accurately modeling neural dynamics in these modalities is crucial for understanding this relationship but is hindered by the high-dimensionality, complex spatiotemporal dependencies, and prevalent behaviorally irrelevant dynamics in these modalities. Existing dynamical models often employ preprocessing steps to obtain low-dimensional representations from neural image modalities. However, this process can discard behaviorally relevant information and miss spatiotemporal structure. We propose SBIND, a novel data-driven deep learning framework to model spatiotemporal dependencies

SUBMITTER: Hosseini M 

PROVIDER: S-EPMC12486059 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature