Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys.

Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged neurostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces.

Hierarchical Bayesian Optimization of Spatiotemporal Neurostimulations for Targeted Motor Outputs.

The development of neurostimulation techniques to evoke motor patterns is an active area of research. It serves as a crucial experimental tool to probe computation in neural circuits, and has applications in neuroprostheses used to aid recovery of motor function after stroke or injury to the nervous system. There are two important challenges when designing algorithms to unveil and control neurostimulation-to-motor correspondences, thereby linking spatiotemporal patterns of neural stimulation to muscle activation: (1) the exploration of motor maps needs to be fast and efficient (exhaustive search is to be avoided for clinical and experimental reasons) (2) online learning needs to be flexible enough to deal with noise and occasional spurious responses.