Investigations on model assumptions.

We developed an extended reciprocity theorem approach to model neuron signals which handles heterogeneous dielectric environments and arbitrary electrode shapes, and use it to study analytically the single fiber action potential. We then established a semi-analytic model that also uses hybrid electromagnetic-electrophysiological simulations to model evoked compound action potential (eCAP) signals from multi-fascicular nerves populated with heterogeneous fiber populations. For validation, model predictions were compared with cuff electrode recordings of activity induced by vagus nerve stimulation in in vivo porcine experiments. The semi-analytic model produces signals that approximate the shape and amplitude of in vivo measurements. It can account for the important variation in the recorded eCAP due to changes in the shape and placement of the stimulus and recording electrodes. We find that partially activated fascicles contribute particularly to the signal, as eCAP contributions from smoothly varying fiber calibers in fully activated ones partially cancel. As a result, eCAP magnitude does not depend monotonically on the stimulation current and recruitment level. Our method can be used to rapidly assess new stimulation and recording setups involving complex nerves and neurovascular bundles, e.g., to maximize signal information content, for closed-loop control in bioelectronic medicine applications, and potentially to non-destructively reconstruct structural and functional nerve topologies through inverse problem solving. In a proof-of-concept study, we demonstrate that parameter optimization can recover the ground-truth distribution of fiber diameters in a simplifed variant of our model.