ArticlePLoS computational biology2016
A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Smart Nanotechnologies for Multimodal Neuromodulation and Brain Interfacing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Assessing Thresholds for Nerve Activation and Action Potential Block Using a Multielectrode Array to Minimize External Stimulation.Bioengineering (Basel, Switzerland) · 2025Article
- Understanding responses to multi-electrode epiretinal stimulation using a biophysical model.Journal of neural engineering · 2025Article
- A Systematic Review to Compare Electrical, Magnetic, and Optogenetic Stimulation for Peripheral Nerve Repair.Journal of hand surgery global online · 2024Review
- Nanoparticle-based optical interfaces for retinal neuromodulation: a review.Frontiers in cellular neuroscience · 2024Review
- Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina.Scientific reports · 2020Article
- Stimulation Strategies for Improving the Resolution of Retinal Prostheses.Frontiers in neuroscience · 2020Review
- Simulation of retinal ganglion cell response using fast independent component analysis.Cognitive neurodynamics · 2018Article
- Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons.PLoS computational biology · 2018Article
- Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.PLoS computational biology · 2017Article
- Subretinal electrical stimulation reveals intact network activity in the blind mouse retina.Journal of neurophysiology · 2016Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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