Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes.

Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon the implantation procedure and the subject's anatomy. Here, we propose an optimization workflow that can guide both the pre-surgical planning and the determination of maximally selective multisite stimulation protocols for implants consisting of several intraneural electrodes, and we characterize its performance in silico. 

Approach. We use hybrid models (HMs) of neuromodulation in conjunction with machine learning and evolutionary optimization to perform for the first time a true in silico optimization of implant geometry, implantation and stimulation protocols using morphological data from the human median nerve at a reduced computational cost.

Main results. Our method allows establishing the optimal geometry of multi-electrode intraneural implants, the optimal number of electrodes to implant, their optimal insertion, and a set of multipolar stimulation protocols that lead in silico to selective activation of all the muscles innervated by the human median nerve. 

Significance. Our work consists of a set of methods and guidelines to use effectively HMs for optimizing personalized neuroprostheses for sensory and motor function restoration, and for bioelectronic medicine.