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pISSN 1226-4512 eISSN 2093-3827


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Original Article

Korean J Physiol Pharmacol 2009; 13(3): 221-227

Published online June 30, 2009

Copyright © Korean J Physiol Pharmacol.

Accurate Representation of Light-intensity Information by the Neural Activities of Independently Firing Retinal Ganglion Cells

Sang Baek Ryu1,3, Jang Hee Ye2,3, Chi Hyun Kim1, Yong Sook Goo2,3, and Kyung Hwan Kim1,3

1Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju 220-710, 2Department of Physiology, Chungbuk National University School of Medicine, Cheongju 361-763, 3Nano Artificial Vision Research Center, Seoul National University Hospital, Seoul 110-744, Korea


For successful restoration of visual function by a visual neural prosthesis such as retinal implant, electrical stimulation should evoke neural responses so that the information on visual input is properly represented. A stimulation strategy, which means a method for generating stimulation waveforms based on visual input, should be developed for this purpose. We proposed to use the decoding of visual input from retinal ganglion cell (RGC) responses for the evaluation of stimulus encoding strategy. This is based on the assumption that reliable encoding of visual information in RGC responses is required to enable successful visual perception. The main purpose of this study was to determine the influence of inter-dependence among stimulated RGCs activities on decoding accuracy. Light intensity variations were decoded from multiunit RGC spike trains using an optimal linear filter. More accurate decoding was possible when different types of RGCs were used together as input. Decoding accuracy was enhanced with independently firing RGCs compared to synchronously firing RGCs. This implies that stimulation of independently-firing RGCs and RGCs of different types may be beneficial for visual function restoration by retinal prosthesis.

Keywords: Retinal prosthesis, Retinal ganglion cell, Multielectrode arrray (MEA), Optimal linear filter, Spike train decoding, Functional connectivity