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A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

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posted on 2024-07-26, 14:24 authored by Matias I. Maturana, Nicholas V. Apollo, Alex E. Hadjinicolaou, David J. Garrett, Shaun L. Cloherty, Tatiana KamenevaTatiana Kameneva, David B. Grayden, Michael R. Ibbotson, Hamish Meffin
Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy.

Funding

Australian Research Council

Feedback control as a tool for enhanced neuroprosthetic stimulation : Australian Research Council | DE120102210

Neural Activity Shaping for Retinal and Cochlear Implants : Australian Research Council | DP140104533

History

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ISSN

1553-7358

Journal title

PLoS Computational Biology

Volume

12

Issue

4

Article number

article no. 1004849

Pagination

e1004849-

Publisher

Public Library of Science

Copyright statement

Copyright © 2016 Maturana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Language

eng

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