Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons.

Matias I Maturana,Michael R Ibbotson,Nicholas V Apollo,Hamish Meffin,Tatiana Kameneva,David B Grayden,Anthony N Burkitt,David J Garrett,Shaun L Cloherty,Ione Fine

doi:10.1371/journal.pcbi.1005997

Abstract

Implantable retinal stimulators activate surviving neurons to restore a sense of vision in people who have lost their photoreceptors through degenerative diseases. Complex spatial and temporal interactions occur in the retina during multi-electrode stimulation. Due to these complexities, most existing implants activate only a few electrodes at a time, limiting the repertoire of available stimulation patterns. Measuring the spatiotemporal interactions between electrodes and retinal cells, and incorporating them into a model may lead to improved stimulation algorithms that exploit the interactions. Here, we present a computational model that accurately predicts both the spatial and temporal nonlinear interactions of multi-electrode stimulation of rat retinal ganglion cells (RGCs). The model was verified using in vitro recordings of ON, OFF, and ON-OFF RGCs in response to subretinal multi-electrode stimulation with biphasic pulses at three stimulation frequencies (10, 20, 30 Hz). The model gives an estimate of each cell’s spatiotemporal electrical receptive fields (ERFs); i.e., the pattern of stimulation leading to excitation or suppression in the neuron. All cells had excitatory ERFs and many also had suppressive sub-regions of their ERFs. We show that the nonlinearities in observed responses arise largely from activation of presynaptic interneurons. When synaptic transmission was blocked, the number of sub-regions of the ERF was reduced, usually to a single excitatory ERF. This suggests that direct cell activation can be modeled accurately by a one-dimensional model with linear interactions between electrodes, whereas indirect stimulation due to summated presynaptic responses is nonlinear.

Highlights

Implantable neural stimulation devices have demonstrated clinical efficacy, from the facilitation of hearing for deaf people using cochlear implants [1] to the treatment of neurological disorders such as epilepsy, Parkinson’s disease, and depression using deep brain stimulation [2]
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
We find that electrical receptive fields (ERFs) often have multiple sub-filters that can be estimated using a Generalized Quadratic Model (GQM) [16], with maximum likelihood methods, to accurately identify the lowdimensional subspace

Summary

Introduction

Implantable neural stimulation devices have demonstrated clinical efficacy, from the facilitation of hearing for deaf people using cochlear implants [1] to the treatment of neurological disorders such as epilepsy, Parkinson’s disease, and depression using deep brain stimulation [2]. The success of future retinal prostheses may benefit greatly from the ability to control spatiotemporal interactions between stimulating electrodes This may allow the design of stimulation strategies that better approximate the spiking patterns of normal vision. A successful approach for extracting visual receptive fields uses models estimated from optical white noise stimulation patterns, which predict retinal responses [7,8,9] and responses in visual cortex [10, 11]. These models use high-dimensional random stimuli and rely on the identification of a low-dimensional stimulus subspace to which the neurons are sensitive. The accuracy of the model depends on the accurate identification of the low-order subspace

Objectives

Results

Discussion

Conclusion