دورية أكاديمية
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Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures.

التفاصيل البيبلوغرافية
العنوان: Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures.
المؤلفون: Zaidi M; Stanford University School of Medicine, Stanford University, Stanford, CA, United States of America.; Neurosurgery, Stanford University, Stanford, CA, United States of America., Aggarwal G; Neurosurgery, Stanford University, Stanford, CA, United States of America.; Electrical Engineering, Stanford University, Stanford, CA, United States of America., Shah NP; Neurosurgery, Stanford University, Stanford, CA, United States of America., Karniol-Tambour O; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America., Goetz G; Neurosurgery, Stanford University, Stanford, CA, United States of America., Madugula SS; Stanford University School of Medicine, Stanford University, Stanford, CA, United States of America.; Neurosciences, Stanford University, Stanford, CA, United States of America., Gogliettino AR; Neurosciences, Stanford University, Stanford, CA, United States of America., Wu EG; Electrical Engineering, Stanford University, Stanford, CA, United States of America., Kling A; Neurosurgery, Stanford University, Stanford, CA, United States of America., Brackbill N; Physics, Stanford University, Stanford, CA, United States of America., Sher A; Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, United States of America., Litke AM; Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, United States of America., Chichilnisky EJ; Neurosurgery, Stanford University, Stanford, CA, United States of America.; Ophthalmology, Stanford University, Stanford, CA, United States of America.
المصدر: Journal of neural engineering [J Neural Eng] 2023 Aug 31; Vol. 20 (4). Date of Electronic Publication: 2023 Aug 31.
نوع المنشور: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural
اللغة: English
بيانات الدورية: Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004-
مواضيع طبية MeSH: Retinal Ganglion Cells*/physiology , Retinal Degeneration*, Animals ; Action Potentials/physiology ; Electric Stimulation/methods ; Retina/physiology ; Macaca
مستخلص: Objective . Retinal implants are designed to stimulate retinal ganglion cells (RGCs) in a way that restores sight to individuals blinded by photoreceptor degeneration. Reproducing high-acuity vision with these devices will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without being able to measure them directly. Here we demonstrate an inference approach that exploits intrinsic electrophysiological features of primate RGCs. Approach. First, ON-parasol and OFF-parasol RGC types were identified using their intrinsic electrical features in large-scale multi-electrode recordings from macaque retina. Then, the electrically inferred somatic location, inferred cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. The accuracy of the cell type classification and of reproducing measured light responses with the model were evaluated. Main results. A cell-type classifier trained on 246 large-scale multi-electrode recordings from 148 retinas achieved 95% mean accuracy on 29 test retinas. In five retinas tested, the inferred models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses (an upper bound). Linear decoding of natural images from predicted RGC activity in one retina showed a mean correlation of 0.55 between decoded and true images, compared to an upper bound of 0.81 using models fitted to light response data. Significance. These results suggest that inference of RGC light response properties from intrinsic features of their electrical activity may be a useful approach for high-fidelity sight restoration. The overall strategy of first inferring cell type from electrical features and then exploiting cell type to help infer natural cell function may also prove broadly useful to neural interfaces.
(© 2023 IOP Publishing Ltd.)
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معلومات مُعتمدة: R01 EY021271 United States EY NEI NIH HHS; F31 EY027166 United States EY NEI NIH HHS; R01 EY032900 United States EY NEI NIH HHS; F30 EY030776 United States EY NEI NIH HHS; P30 EY019005 United States EY NEI NIH HHS; R01 EY029247 United States EY NEI NIH HHS
فهرسة مساهمة: Keywords: artificial vision; brain computer interface; cell type; neural interface; retina; retinal implant; retinal interface
تواريخ الأحداث: Date Created: 20230711 Date Completed: 20230901 Latest Revision: 20240505
رمز التحديث: 20240505
مُعرف محوري في PubMed: PMC11067857
DOI: 10.1088/1741-2552/ace657
PMID: 37433293
قاعدة البيانات: MEDLINE
الوصف
تدمد:1741-2552
DOI:10.1088/1741-2552/ace657