Hybrid and fast: A novel in silico approach with reduced computational cost to predict failures of in vivo needle-based implantations

Abstract

Penetrating neural interfaces, connecting peripheral nerves to robotic devices (e.g., hand prostheses), could be inserted through tungsten needles, which are able to minimize damages and scarring due to the puncture wounds. Unfortunately, puncturing needles may fail independently on the material fracture toughness. In addition, independently on internal biotic causes, needles’ performances may decrease during in vivo trials. External biotic causes seems to be related to these effects, even if the exact genesis of phenomena, decreasing the in vivo reliability, is still partially unknown. Therefore, this work provides a hybrid computational approach, simultaneously using theoretical relationships and novel fast silico models of nerves. This framework is able to lower computational times needed to predict in vivo performances by using in vitro reliability and local differences between in vivo and in vitro mechanical response of nerves.