Abstract: A method for preferential system identification (PSID) for modeling neural dynamics of a brain includes extracting behaviorally relevant latent brain states via a projection of future behavior onto past neural activity. The method further includes identifying, based on the extracted latent brain states, model parameters for a linear state space dynamic model of neural activity. An extension of PSID includes accounting for an effect of external inputs to the brain by using oblique projections along the external inputs instead of orthogonal projections. PSID may be extended to apply to any primary signals and secondary signals in place of the neural activity and the behavior. In some embodiments, the extracting and the identifying may be performed using a numerical optimization of recurrent neural networks (RNN) instead of the projection.

Abstract: A method for nonlinear modeling, decoding, and control of neural dynamics includes identifying, based on neural time-series samples, a type of a manifold as a base for a neural model. The method further includes learning, based on a covering space, a dynamic model that is fit on the manifold to create the neural model. The method further includes creating, using the neural model, a geometric decoder and a geometric controller.

Abstract: A system for controlling a state of anesthesia of a patient. The system includes a neural sensor configured to be coupled to a patient and generate an output signal indicative of neural activity of the patient; and an electronic processor. The electronic processor is coupled to the neural sensor and to an infusion pump, and is configured to receive the output signal from the neural sensor, estimate a drug concentration of the patient based on a model including a first model parameter, and estimate a second model parameter based on the output signal from the neural sensor. The electronic processor is also configured to update the model for estimating the drug concentration of the patient with the second model parameter, and output control signals to the infusion pump to deliver a drug, the output signals being based on the estimated drug concentration for the patient and estimates of the parameters.

Abstract: A system for controlling a state of anesthesia of a patient. The system includes a neural sensor configured to be coupled to a patient and generate an output signal indicative of neural activity of the patient; and an electronic processor. The electronic processor is coupled to the neural sensor and to an infusion pump, and is configured to receive the output signal from the neural sensor, estimate a drug concentration of the patient based on a model including a first model parameter, and estimate a second model parameter based on the output signal from the neural sensor. The electronic processor is also configured to update the model for estimating the drug concentration of the patient with the second model parameter, and output control signals to the infusion pump to deliver a drug, the output signals being based on the estimated drug concentration for the patient and estimates of the parameters.