Real-Time Optimization of Retinal Ganglion Cell Spatial Activity in Response to Epiretinal Stimulation.

Retinal prostheses aim to improve visual perception in patients blinded by photoreceptor degeneration. However, shape and letter perception with these devices is currently limited due to low spatial resolution. Previous research has shown the retinal ganglion cell (RGC) spatial activity and phosphene shapes can vary due to the complexity of retina structure and electrode-retina interactions. Visual percepts elicited by single electrodes differ in size and shapes for different electrodes within the same subject, resulting in interference between phosphenes and an unclear image. Prior work has shown that better patient outcomes correlate with spatially separate phosphenes. In this study we use calcium imaging, in vitro retina, neural networks (NN), and an optimization algorithm to demonstrate a method to iteratively search for optimal stimulation parameters that create focal RGC activation. Our findings indicate that we can converge to stimulation parameters that result in focal RGC activation by sampling less than 1/3 of the parameter space. A similar process implemented clinically can reduce time required for optimizing implant operation and enable personalized fitting of retinal prostheses.

Genetic Algorithm For Fitting Cardiac Cell Biophysical Model Formulations.

Modeling cardiac cell electrophysiology relies on fitting model equations to experimental data obtained under voltage/current clamping conditions. The fitting procedure for these often-nonlinear ionic current equations are mostly executed by trial-and-error by hand or by gradient-based optimization approaches. These methods, though sometimes sufficient at converging at optimal solutions is based on the premise that the characteristic objective function is convex, which often does not apply to cardiac model equations. Meta-heuristic methods, such as evolutionary algorithms and particle swarm algorithms, have proven resilient against early convergence to local optima and saddle-point parameter solutions. This work presents a genetic algorithm-based approach for fitting the adult cardiomyocyte biophysical model formulations to the experimental data obtained in human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM). Specifically, whole-cell patch clamp ionic current data of rapid delayed rectifier potassium current, IKr, transient outward potassium current, Ito and hyperpolarization-activated current, If, was used for fitting. Using a two-point crossover scheme along with initial population and mutation constraints randomly selected from a uniformly distributed constrained parameter space, near-optimal fitting was achieved with R2 values (n = 5) of 0.9960±0.0007, 0.9995±0.0002, and 0.9974±0.0014 for IKr, Ito and If respectively.