Learning nonlinear embeddings for control of neural dynamics

Control of neural activity could provide a way to study causal interactions within neural populations and develop adaptive interventions for neurological disorders. However, doing so is hindered by the complexity of neural population dynamics and their response to external inputs. Prior work on neural control has largely relied on linear or generalized linear models, whose simplicity enables tractable estimation and control design, but which may not capture sufficient complexity in neural dynamics for accurate control. Here, we develop a model that learns nonlinear embeddings of neural population activity with linear or locally linear latent dynamics using control-focused objectives. Combined with recursive state estimation, this model captures nonlinearity while enabling tractable model-based control. We validate our model and controller in simulation, across benchmark systems relevant to neuroscience, spanning both linear and nonlinear dynamics. Across these systems, our models outperform the baselines in stabilizing activity at desired target setpoints and in restoring movement trajectories following simulated lesions. Together, these simulation results show that learning control-relevant embeddings and latent dynamics provides a practical approach toward future systems for model-based control of nonlinear neural population activity.