Optimizing Neuroprosthetic Therapies via Autonomous Learning Agents

Abstract

Neural stimulation can alleviate or even reverse paralysis and sensory deficits. Rapid technological advancements bring the possibility to develop complex and refined patterns of neurostimulation. However, multipronged interventions with high-density neural interfaces will require algorithmic frameworks to handle optimization in large parameter spaces. Here, we used an algorithmic class, Gaussian-Process (GP)-based Bayesian Optimization (BO), to solve this online problem. We show that GP-BO can efficiently explore the neurostimulation parameters’ space, exceeding extensive search performance after testing only a fraction of the possible combinations. It can quickly optimize multi-channel neurostimulation across diverse biological targets (brain and spinal cord), animal models (rats and non-human primates), in healthy and injured subjects. Moreover, since BO can embed and improve ‘prior’ expert/clinical knowledge, the performance can be dramatically enhanced even further. These results support broad establishment of learning agents as a structural part of neuroprosthetic design, enabling therapeutic personalization and maximization of intervention effectiveness.

Keywords: Machine-learning, Brain-machine interface, motor cortex, spinal cord injury, Bayesian optimization, neurostimulation