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The nervous system communicates via electrical signals. Electrical neurostimulation is obtained by positioning electrodes in contact with brain, spinal cord or nerves and delivering stimuli that will modulate neuronal activity. This powerful technique allows causal investigation of neural circuits, enabling neuroscientific discovery. It also constitutes the biophysical foundation of a class of medical interventions.

Neurostimulation always requires precise adjustment of several stimulation parameters, such as the spatial location of the stimulus, the timing, as well as the frequency of stimulus delivery. Even in the most cutting-edge applications, stimulation tuning has been almost exclusively handled manually. The lack of algorithmic frameworks to control and optimize neurostimulation has hindered scientific discovery.

Our program is to transform neurostimulation by introducing an advanced autonomous control layer. We use Gaussian Process-based Bayesian Optimization (GPBO) as an algorithmic framework to tailor and personalize neurostimulation to each individual implant.

We show that this framework could be scaled, via algorithmic novelties, to unprecedented neurostimulation steering capacities:

1) from solely stationary to new non-stationary optimization options;

2) from single target to multi-target optimization;

3) from simple outputs to sequences of stimuli.

This work will equip neuroscientists and designers of medical technology with a toolbox of optimization methods to scale the next generation of medical technologies well beyond the limits of the present constrained control.