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The method, which enables efficient evaluation of a nerve's response to millions of electrode designs, is an integral step toward building more accurate and capable electrodes to stimulate nerves and thereby enable people with paralysis or amputated limbs better control of movement.
To increase the accuracy of the results, the researchers included a key parameter overlooked in past mathematical approaches that were equally fast, but inaccurate. With the new techniques, electrode design can be optimized using advanced algorithms based on natural genetics.
"We believe this will allow the next generation of computer-aided development of electrodes," said Dustin Tyler, professor Case School of Engineering. Since his graduate school days, Tyler has been developing electrodes to stimulate nerves in paralysed patients and amputees. Taking the large step from animal models to human clinical trials can be improved with better computer modeling, he said.
"Finding the optimal way to stimulate a nerve is kind of like the 'travelling salesman' trying to figure out which is the most efficient route through a group of cities," Tyler said. Mapping each possible route and figuring the time spent on the road is very difficult to do with a simple equation.
The genetic algorithm mimics the process of natural selection, gene recombination and mutation seen in nature. Or, in this case, takes into account which portions of a neuron to stimulate, how much, with how many points of contact, and more variables. By adding a variable: the magnitude of the voltage outside the cell produced by the electrode, Tyler's group raised the accuracy beyond current techniques.
Their method was developed specifically for peripheral motor nerve axons. Nerves cells with different structures, such as those in the brain, spinal cord, or organs are still being investigated. The researchers are now developing parameters that would take into account these variations in structure to extend the method to work for all of them, further cutting time needed to develop accurate models.
MEDICA.de; Source: University of Minnesota