Objective Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main Results The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

We acknowledge the Duke Compute Cluster for their assistance with our simulations. We thank Danielle Degoski for her assistance with experiments. Conflict of InterestA. No (State 'Authors report no conflict of interest') B. Yes (Please explain) T.Z. and R.E. are employees of Boston Scientific Corporation. J.E.G, N.D.T, T.Z. and W.M.G. have received royalty payments from Boston Scientific Corporation for licensed I.P. W.M.G received compensation from Boston Scientific as a member of the Neuromodulation Scientific Advisory Board. T.Z. and R.E. own Boston Scientific stock and R.E owns stock in NeuroPace.

Biophysically-based computational models of nerve fibers are important tools for designing electrical stimulation therapies, investigating drugs that affect ion channels, and studying diseases that affect neurons. Although peripheral nerves are primarily composed of unmyelinated axons (i.e., C-fibers), most modeling efforts focused on myelinated axons. We implemented the single-compartment model of vagal afferents from Schild et al. 1994 and extended the model into a multi-compartment axon, presenting the first cable model of a C-fiber vagal afferent. We also implemented the updated parameters from Schild and Kunze 1997. We compared the responses of these novel models to three published models of unmyelinated axons (Rattay and Aberham 1993; Sundt et al. 2015; Tigerholm et al. 2014) and to experimental data from single fiber recordings. Comparing Schild et al. 1994 and 1997 revealed that differences in rest potential and action potential shape were driven by changes in maximum conductances rather than changes in sodium channel dynamics. Comparing the five model axons, the conduction speeds and strength-duration responses were largely within expected ranges, but none of the models captured the experimental threshold recovery cycle-including a complete absence of late subnormality in the models-and their action potential shapes varied dramatically. The Tigerholm et al. 2014 model best reproduced the experimental data, but these modeling efforts make clear that additional data are needed to parameterize and validate future models of autonomic C-fibers.

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