Optimization of return electrodes in neurostimulating arrays.

نویسندگان

  • Thomas Flores
  • Georges Goetz
  • Xin Lei
  • Daniel Palanker
چکیده

OBJECTIVE High resolution visual prostheses require dense stimulating arrays with localized inputs of individual electrodes. We study the electric field produced by multielectrode arrays in electrolyte to determine an optimal configuration of return electrodes and activation sequence. APPROACH To determine the boundary conditions for computation of the electric field in electrolyte, we assessed current dynamics using an equivalent circuit of a multielectrode array with interleaved return electrodes. The electric field modeled with two different boundary conditions derived from the equivalent circuit was then compared to measurements of electric potential in electrolyte. To assess the effect of return electrode configuration on retinal stimulation, we transformed the computed electric fields into retinal response using a model of neural network-mediated stimulation. MAIN RESULTS Electric currents at the capacitive electrode-electrolyte interface redistribute over time, so that boundary conditions transition from equipotential surfaces at the beginning of the pulse to uniform current density in steady state. Experimental measurements confirmed that, in steady state, the boundary condition corresponds to a uniform current density on electrode surfaces. Arrays with local return electrodes exhibit improved field confinement and can elicit stronger network-mediated retinal response compared to those with a common remote return. Connecting local return electrodes enhances the field penetration depth and allows reducing the return electrode area. Sequential activation of the pixels in large monopolar arrays reduces electrical cross-talk and improves the contrast in pattern stimulation. SIGNIFICANCE Accurate modeling of multielectrode arrays helps optimize the electrode configuration to maximize the spatial resolution, contrast and dynamic range of retinal prostheses.

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عنوان ژورنال:
  • Journal of neural engineering

دوره 13 3  شماره 

صفحات  -

تاریخ انتشار 2016