Title | ||
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Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2018 | 10.1371/journal.pcbi.1005997 | PLOS COMPUTATIONAL BIOLOGY |
Field | DocType | Volume |
Receptive field,Retinal ganglion,Neuroscience,Neurotransmission,Biology,Retina,Excitatory postsynaptic potential,Bioinformatics,Retinal,Neuron,Stimulation | Journal | 14 |
Issue | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
6 | 9 |
Name | Order | Citations | PageRank |
---|---|---|---|
Matias I. Maturana | 1 | 2 | 2.19 |
Nicholas V. Apollo | 2 | 0 | 1.01 |
David J Garrett | 3 | 0 | 1.69 |
Tatiana Kameneva | 4 | 14 | 7.38 |
Shaun L. Cloherty | 5 | 21 | 9.18 |
David B. Grayden | 6 | 254 | 29.89 |
Anthony N. Burkitt | 7 | 487 | 46.71 |
Michael R. Ibbotson | 8 | 11 | 4.22 |
Hamish Meffin | 9 | 102 | 14.94 |