BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.

ABSTRACT: Neural populations exhibit complex recurrent structures that drive behavior, while continuously receiving and integrating external inputs from sensory stimuli, upstream regions, and neurostimulation. However, neural populations are often modeled as autonomous dynamical systems, with little consideration given to the influence of external inputs that shape the population activity and behavioral outcomes. Here, we introduce BRAID, a deep learning framework that models nonlinear neural dynamics underlying behavior while explicitly incorporating any measured external inputs. Our method disentangles intrinsic recurrent neural population dynamics from the effects of inputs by including a forecasting objective within input-driven recurrent neural networks. BRAID further prioritizes the learning o

SUBMITTER: Vahidi P 

PROVIDER: S-EPMC12486053 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature