An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

Abstract

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.

A wrappable microwire electrode for awake, chronic interfacing with small diameter autonomic peripheral nerves

“…Therefore, this group is developing flexible, high density electrodes to chronically interface with the vagus nerve in the awake state (Caravaca et al 2017;Li et al 2017). These implants will increase the amount of information obtained from the nerve and also can be used to create unique stimulation patterns to potentially target different nerve fibers (Cassar et al 2017).…”

Printable microscale interfaces for long-term peripheral nerve mapping and precision control

“…Additionally, the integration of non-planar electrode arrays that provide improved electrical access to the nerve -either by increasing efficacy of contact with the epineurim (Robinson et al, 2012;Shmoel et al, 2016) or by penetrating into the intraneural space (Angle et al, 2015;Gillis et al, 2018;Vitale et al, 2015) -may allow for greater recording and stimulating specificity with low risk of increased host tissue trauma (Salatino et al, 2017). And finally, we showed that current-steering-based stimulation can achieve a high degree of modulatory specificity within the small geometry of the nanoclip, but our results also highlight the challenge of identifying multi-channel stimulation parameters that will produce and maintain a specific physiological or end effector state (Cassar et al, 2017;Grill et al, 1991;Veraart et al, 1993). Thus, full realization of multi-channel neuromodulation for precise control of PNS activity will require closed-loop, unsupervised methods for mapping stimulation patterns to functional outputs such that robust control of end state is possible.…”

Review of Nature-Inspired Optimization Algorithms Applied in Civil Engineering

“…The GA is a stochastic technique to find a solution that mimics the principles of natural genetics [76,77]. The GA is inspired by biological evolution, Charles Darwin's theory of natural evolution [78], which uses propagation, random mutation, genetic diversity, and natural selection [79]. This algorithm reflects the process of natural selection where the best individuals are selected for breeding to give offspring in the next generation [72].…”