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Browsing Electrical Engineering and Computer Science by Subject "Adaptive-threshold spike detection"
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Item Open Access Closed-Loop Highly-Scalable Retinal Implant with Fully-Analog ED-Based Adaptive-Threshold Spike Detection and Poisson-Coded Temporally-Distributed Optogenetic Stimulation(2024-07-18) Yousefi, Tayebeh; Kassiri, HosseinIntraocular stimulators show promise for treating retinal degeneration by restoring visual input to the damaged retina. This is achieved by capturing images with a wearable camera and accordingly stimulating remaining retinal cells, effectively bypassing dysfunctional photoreceptors. State-of-the-art retinal stimulators face a major challenge due to the lack of cell-type specificity of electrical stimulation (activating both ON and OFF pathways in the retina) leading to limited visual perception due to sending contradictory messages to the brain. This fundamental limit motivated us to investigate the development of an optogenetic-based retinal prosthesis, that uses promoter opsins for selective activation of ON bipolar cells, offering a more natural vision restoration. In developing such a device, the first challenge we faced was optimizing stimulation strategy for optimal therapeutic efficacy. Responding to this challenge, we first present a retina-inspired computational framework to evaluate and optimize an optogenetic epi-retinal neurostimulator. This framework reveals that optical stimulation, compared to electrical stimulation, provides superior visual perception, which improves with increased μLED array resolution. The framework also explores optical stimulation factors and μLED specifications like light intensity and wavelength spatial resolution and light divergence. A critical issue in optogenetics is controlling opsin distribution, as uneven distribution affects light sensitivity across the retina. Variations in tissue properties and fluid dynamics introduce unpredictability in stimulation effectiveness. Our solution to this issue includes a scalable optogenetic stimulator IC, which features channel-specific closed-loop calibration for defining the optimal stimulation intensity using a temporally adaptive-threshold spike detection circuit. The second challenge we addressed was scalability, and by association, energy efficiency of the device. Scaling implantable stimulators is limited by instantaneous power demands during multi-channel stimulation. We address this by exploiting opsins’ sensitivity to integrated optical energy, using Poisson coding, temporally-distributed stimulation to evenly distribute the stimulation power consumption, enabled by our raster scanning technique for efficient μLED addressing. This reduces wireless data communication requirements, and significantly reduces IC-to-optrode interconnections, making large-scale implementation feasible. Our wireless and battery-less stimulator implant comprises blocks for optical stimulation, fully-adaptive spike detection, and closed-loop calibration. It calibrates light intensity for each μLED row based on recorded spiking activity.