Abstract
Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment "input" to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This "myopic" controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a step-wise manner, omitting the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this control's utility, two examples with distinctly different applications in neuroscience are studied. First an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders. Second, we show the myopic control's utility to probe the causal dynamics of cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence.
