Abstract:
OBJECTIVE:Over the past two decades retinal prostheses have made major strides in restoring functional vision to patients blinded by diseases such as retinitis pigmentosa. Presently, implants use single pulses to activate the retina. Though this stimulation paradigm has proved beneficial to patients, an unresolved problem is the inability to selectively stimulate the on and off visual pathways. To this end our goal was to test, using white noise, voltage-controlled, cathodic, monophasic pulse stimulation, whether different retinal ganglion cell (RGC) types in the wild type retina have different electrical input filters. This is an important precursor to addressing pathway-selective stimulation. APPROACH:Using full-field visual flash and electrical and visual Gaussian noise stimulation, combined with the technique of spike-triggered averaging (STA), we calculate the electrical and visual input filters for different types of RGCs (classified as on, off or on-off based on their response to the flash stimuli). MAIN RESULTS:Examining the STAs, we found that the spiking activity of on cells during electrical stimulation correlates with a decrease in the voltage magnitude preceding a spike, while the spiking activity of off cells correlates with an increase in the voltage preceding a spike. No electrical preference was found for on-off cells. Comparing STAs of wild type and rd10 mice revealed narrower electrical STA deflections with shorter latencies in rd10. SIGNIFICANCE:This study is the first comparison of visual cell types and their corresponding temporal electrical input filters in the retina. The altered input filters in degenerated rd10 retinas are consistent with photoreceptor stimulation underlying visual type-specific electrical STA shapes in wild type retina. It is therefore conceivable that existing implants could target partially degenerated photoreceptors that have only lost their outer segments, but not somas, to selectively activate the on and off visual pathways.
journal_name
J Neural Engjournal_title
Journal of neural engineeringauthors
Sekhar S,Jalligampala A,Zrenner E,Rathbun DLdoi
10.1088/1741-2552/aa722csubject
Has Abstractpub_date
2017-08-01 00:00:00pages
046017issue
4eissn
1741-2560issn
1741-2552journal_volume
14pub_type
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