Model-based optogenetic stimulation to regulate beta oscillations in Parkinsonian neural networks.

Optogenetic stimulation, an effective stimulation technique, is applied to the treatment of Parkinson's disease (PD) to compete with the current neuromodulation technology that focuses on the electrical stimulation. Using the cortical-thalamic-basal ganglia model, we systematically study the effect of optogenetic stimulation on pathological parkinsonian rhythmic neural activity. Based on the experimental studies, four types of neurons are selected as stimulation targets.

A review of computational modeling and deep brain stimulation: applications to Parkinson's disease.

Biophysical computational models are complementary to experiments and theories, providing powerful tools for the study of neurological diseases. The focus of this review is the dynamic modeling and control strategies of Parkinson's disease (PD). In previous studies, the development of parkinsonian network dynamics modeling has made great progress.

Distributed Model Predictive Control for Linear-Quadratic Performance and Consensus State Optimization of Multiagent Systems.

The optimal consensus problem of asynchronous sampling single-integrator and double-integrator multiagent systems is solved by distributed model predictive control (MPC) algorithms proposed in this article. In each predictive horizon, the finite-time linear-quadratic performance is minimized distributively by the control input with consensus state optimization. The MPC technique is then utilized to extend the optimal control sequence to the case of an infinite horizon.