Structured Learning of Behaviorally Relevant Neural Dynamics in Multimodal Neural Time-series

Multimodal neural data can enable a more complete understanding of brain dynamics underlying behavior. Modalities such as spiking activity, local field potentials (LFPs), and behavior capture diverse spatiotemporal aspects of brain processes, yet prior neural-behavioral models that separate sources of variability, rely on a single neural modality. By leveraging these complementary strengths, multimodal neural fusion can provide a unified, rich representation of brain-behavior processes and address the limitations of single-modality analyses. Here we develop BREM-NET, a nonlinear multimodal dynamical model that integrates behavior with multiple neural modalities having distinct statistical characteristics and temporal resolutions. BREM-NET performs multimodal neural fusion during inference while separating behaviorally relevant and neural-specific dynamics via a structured learning framework. In a public multimodal non-human primate dataset with simultaneous spiking and local field potential recordings during a 2D reaching task, BREM-NET separates these two types of dynamics and learns them more accurately, as reflected in stronger latent alignment with behavior, better behavior decoding, and better neural prediction, and it retains these capabilities when neural modalities are asynchronous or suffer dropouts, which are major challenges in real-world recordings. This framework provides a new tool for studying behaviorally relevant neural computations across spatiotemporal scales of brain activity.