A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator With Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

We present a fully implantable, inductively powered optogenetic stimulator that enhances stimulation efficacy and pathway specificity while maximizing energy efficiency and channel-count scalability. By leveraging opsins' photon integration properties with raster scanning and Poisson-coded stimulation, we achieve a uniform power profile and reduce wiring complexity, enabling a scalable system that supports more stimulation channels without compromising safety or functionality, improving prosthetic vision resolution. We also employed a compact and power-efficient (0.026 and 1.02 W overhead) SNR-boosted ADC-less spike detection circuit to adapt each LED's light intensity based on real-time feedback from RGC spiking cells. This closed-loop adaptivity adjusts stimulation to opsin distribution variations, over time and across different patients, ensuring effective and consistent stimulation across patients, enhancing both energy efficiency and visual perception quality. The 3 3 IC, fabricated in 180nm CMOS, is coupled with a 100-channel custom optrode array fabricated using an InGaN process on a sapphire substrate. Experimental results demonstrate circuit-level performance, system-level efficacy, and in-vitro validation. Comparison tables highlight our work's advantages over state-of-the-art implantable spike detection systems and retinal prostheses.

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Publication Details

Journal
IEEE Transactions on Biomedical Circuits and Systems
Published
2024-10-31
DOI
https://doi.org/10.1109/tbcas.2024.3488713
Citations
2
Primary Topic
Photoreceptor and optogenetics research
Type
article
Field-Weighted Citation Impact
0.54
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article

A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator With Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

Georg Zoidl, Tayebeh Yousefi, Hossein Kassiri
2 citations
IEEE Transactions on Biomedical Circuits and Systems
Photoreceptor and optogenetics research
0.54
article

A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator With Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

Georg Zoidl, Tayebeh Yousefi, Hossein Kassiri
article en
2 citations

Abstract

We present a fully implantable, inductively powered optogenetic stimulator that enhances stimulation efficacy and pathway specificity while maximizing energy efficiency and channel-count scalability. By leveraging opsins' photon integration properties with raster scanning and Poisson-coded stimulation, we achieve a uniform power profile and reduce wiring complexity, enabling a scalable system that supports more stimulation channels without compromising safety or functionality, improving prosthetic vision resolution. We also employed a compact and power-efficient (0.026 and 1.02 W overhead) SNR-boosted ADC-less spike detection circuit to adapt each LED's light intensity based on real-time feedback from RGC spiking cells. This closed-loop adaptivity adjusts stimulation to opsin distribution variations, over time and across different patients, ensuring effective and consistent stimulation across patients, enhancing both energy efficiency and visual perception quality. The 3 3 IC, fabricated in 180nm CMOS, is coupled with a 100-channel custom optrode array fabricated using an InGaN process on a sapphire substrate. Experimental results demonstrate circuit-level performance, system-level efficacy, and in-vitro validation. Comparison tables highlight our work's advantages over state-of-the-art implantable spike detection systems and retinal prostheses.

IEEE Transactions on Biomedical Circuits and SystemsVol. 18(6)
York University (CA)
Affordable and clean energy
Openalex Percentile: Top 34%
Photoreceptor and optogenetics research
0.54
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A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator With Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration — Georg Zoidl, Tayebeh Yousefi, et al. · IEEE Transactions on Biomedical Circuits and Systems (2024) | TGRS Research Map | TGRS