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

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.

Spike-triggered average electrical stimuli as input filters for bionic vision—a perspective

“…In contrast to our focus on stimulus dimensionality in time, many other researchers have examined the spatial dimension of the eRF with noise stimuli (Maturana et al 2016;Lorach et al 2015). The use of noise stimuli allows powerful computational methods like the STA, spike-triggered covariance (STC), and linear-nonlinear-linear modeling to be used.…”

Neural Responses to Multielectrode Stimulation of Healthy and Degenerate Retina

“…To characterize differences in cortical responses to multielectrode stimulation in both the healthy and degenerate retinae, a linear-nonlinear model was fitted to the Gaussian white noise stimulation responses. [43][44][45] This model provides information about the response of each cortical recording site to stimulation of multiple electrodes, including which electrodes significantly affect each channel, and the nonlinear characteristics, such as response thresholds and saturation levels. The model comprises two spatial linear filters (V P and V N ), followed by two parallel static nonlinearities (g P and g N ) for each recording channel.…”

Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina

“…Recently, white noise composed of biphasic electrical current pulses with amplitudes drawn from a Gaussian distribution was used to map the spatiotemporal electrical receptive fields of rat retinal ganglion cells. Subsequently fit linear-nonlinear models accurately predicted RGC responses to electrical stimulation 38,39 . Linear filters of mouse RGCs could also be recovered with a spatially uniform white noise stimulus consisting of normally distributed subthreshold voltage pulses 40,41 .…”