Topic: nonlinear state-space model

Motor and premotor cortical activities reflect motor planning and execution. For stereotyped movements, the internal cortical state should be predictive of subsequent motor commands. We developed a statistical method to infer a nonlinear state-space model that extracts an effective systems-level description of neural dynamics from population recordings. Without the knowledge of the task, target, or motor behavior, the inferred model can forecast neural activity at a single-trial level. Our novel inference algorithm performs approximate Bayesian filtering and smoothing within a structured varia