• Title/Summary/Keyword: Multielectrode Recording

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Functional Connectivity Map of Retinal Ganglion Cells for Retinal Prosthesis

  • Ye, Jang-Hee;Ryu, Sang-Baek;Kim, Kyung-Hwan;Goo, Yong-Sook
    • The Korean Journal of Physiology and Pharmacology
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    • v.12 no.6
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    • pp.307-314
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    • 2008
  • Retinal prostheses are being developed to restore vision for the blind with retinal diseases such as retinitis pigmentosa (RP) or age-related macular degeneration (AMD). Among the many issues for prosthesis development, stimulation encoding strategy is one of the most essential electrophysiological issues. The more we understand the retinal circuitry how it encodes and processes visual information, the greater it could help decide stimulation encoding strategy for retinal prosthesis. Therefore, we examined how retinal ganglion cells (RGCs) in in-vitro retinal preparation act together to encode a visual scene with multielectrode array (MEA). Simultaneous recording of many RGCs with MEA showed that nearby neurons often fired synchronously, with spike delays mostly within 1 ms range. This synchronized firing - narrow correlation - was blocked by gap junction blocker, heptanol, but not by glutamatergic synapse blocker, kynurenic acid. By tracking down all the RGC pairs which showed narrow correlation, we could harvest 40 functional connectivity maps of RGCs which showed the cell cluster firing together. We suggest that finding functional connectivity map would be useful in stimulation encoding strategy for the retinal prosthesis since stimulating the cluster of RGCs would be more efficient than separately stimulating each individual RGC.

The advantage of topographic prominence-adopted filter for the detection of short-latency spikes of retinal ganglion cells

  • Ahn, Jungryul;Choi, Myoung-Hwan;Kim, Kwangsoo;Senok, Solomon S.;Cho, Dong-il Dan;Koo, Kyo-in;Goo, Yongsook
    • The Korean Journal of Physiology and Pharmacology
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    • v.21 no.5
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    • pp.555-563
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    • 2017
  • Electrical stimulation through retinal prosthesis elicits both short and long-latency retinal ganglion cell (RGC) spikes. Because the short-latency RGC spike is usually obscured by electrical stimulus artifact, it is very important to isolate spike from stimulus artifact. Previously, we showed that topographic prominence (TP) discriminator based algorithm is valid and useful for artifact subtraction. In this study, we compared the performance of forward backward (FB) filter only vs. TP-adopted FB filter for artifact subtraction. From the extracted retinae of rd1 mice, we recorded RGC spikes with $8{\times}8$ multielectrode array (MEA). The recorded signals were classified into four groups by distances between the stimulation and recording electrodes on MEA (200-400, 400-600, 600-800, $800-1000{\mu}m$). Fifty cathodic phase-$1^{st}$ biphasic current pulses (duration $500{\mu}s$, intensity 5, 10, 20, 30, 40, 50, $60{\mu}A$) were applied at every 1 sec. We compared false positive error and false negative error in FB filter and TP-adopted FB filter. By implementing TP-adopted FB filter, short-latency spike can be detected better regarding sensitivity and specificity for detecting spikes regardless of the strength of stimulus and the distance between stimulus and recording electrodes.

Waveform Sorting of Rabbit Retinal Ganglion Cell Activity Recorded with Multielectrode Array (다채널전극으로 기록한 토끼 망막신경절세포의 활동전위 파형 구분)

  • Jin Gye Hwan;Lee Tae Soo;Goo Yang Sook
    • Progress in Medical Physics
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    • v.16 no.3
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    • pp.148-154
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    • 2005
  • Since the output of retina for visual stimulus is carried by neurons of very diverse functional properties, it is not adequate to use conventional single electrode for recording the retinal action potential. For this purpose, we used newly developed multichannel recording system for monitoring the simultaneous electrical activities of many neurons in a functioning piece of retina. Retinal action potentials are recorded with an extra-cellular planar array of 60 microelectrodes. In studying the collective activity of the ganglion cell population it is essential to recognize basic functional distinctions between individual neurons. Therefore, it is necessary to detect and to classify the action potential of each ganglion cell out of mixed signal. We programmed M-files with MATLAB for this sorting process. This processing is mandatory for further analysis, e.g. poststimulus time histogram (PSTH), auto-correlogram, and cross-correlogram. We established MATLAB based protocol for waveform classification and verified that this approach was effective as an initial spike sorting method.

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