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Miniaturization of the neuromodulation system is important for non-invasive or sub-invasive optogenetic application. This work presents an optimized wireless power transfer (WPT) system integrated with an on-chip rectification circuitry and an off-chip stimulation circuitry for optogenetic stimulation of freely moving rodents. The proposed WPT system is built using parallel transmitter (TX) coils on printed circuit board (PCB) and wire-wound based receiver (RX) coil followed by a seven-stage voltage doubler and a low dropout regulator (LDO) circuit designed in 180 nm standard Complementary Metal Oxide Semiconductor (CMOS) process. A pulse stimulation is used to stimulate the neurons which is generated using a commercially available off-the-shelf (COTS) components based oscillator circuit. The intensity of the stimulation is controlled by using a COTS based LED driver circuit which controls the current through the μ LED. The total dimension of the RX coil is 8 mm × 3.4 mm. The maximum power transfer efficiency (PTE) of the proposed WPT system is ∼ 35% and the power conversion efficiency (PCE) of the rectifier is 52%. The proposed system with reconfigurable stimulation frequency is suitable for exciting different brain areas for long-term health monitoring.
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Reinforcement Learning-Guided Optogenetic Stimulation Policies for Robust Functional Network Discovery
Optogenetic stimulation has opened up a new avenue to probe neuronal circuitry at high spatiotemporal resolutions. A key challenge in optogenetic stimulation is deciding which subset out of thousands of neurons should be stimulated to elicit a desired network activation or affect behavior. In this work, we introduce a reinforcement learning approach to adaptively narrow down the multitude of stimulation possibilities and robustly identify Granger causal networks that underlie neuronal activity. We use realistic simulations with different underlying circuitry to show the effectiveness of reinforcement learning in identifying an optimal policy for selecting stimulation targets.
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- PAR ID:
- 10546497
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-4485-1
- Page Range / eLocation ID:
- 13271 to 13275
- Format(s):
- Medium: X
- Location:
- 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP48485.2024.10446382
