TY - JOUR
T1 - A Biomimetic, SoC-Based Neural Stimulator for Novel Arbitrary-Waveform Stimulation Protocols
AU - Culaclii, Stanislav
AU - Wang, Po Min
AU - Taccola, Giuliano
AU - Yang, William
AU - Bailey, Brett
AU - Chen, Yan Peng
AU - Lo, Yi Kai
AU - Liu, Wentai
N1 - Publisher Copyright:
© Copyright © 2021 Culaclii, Wang, Taccola, Yang, Bailey, Chen, Lo and Liu.
PY - 2021/7/29
Y1 - 2021/7/29
N2 - Novel neural stimulation protocols mimicking biological signals and patterns have demonstrated significant advantages as compared to traditional protocols based on uniform periodic square pulses. At the same time, the treatments for neural disorders which employ such protocols require the stimulator to be integrated into miniaturized wearable devices or implantable neural prostheses. Unfortunately, most miniaturized stimulator designs show none or very limited ability to deliver biomimetic protocols due to the architecture of their control logic, which generates the waveform. Most such designs are integrated into a single System-on-Chip (SoC) for the size reduction and the option to implement them as neural implants. But their on-chip stimulation controllers are fixed and limited in memory and computing power, preventing them from accommodating the amplitude and timing variances, and the waveform data parameters necessary to output biomimetic stimulation. To that end, a new stimulator architecture is proposed, which distributes the control logic over three component tiers – software, microcontroller firmware and digital circuits of the SoC, which is compatible with existing and future biomimetic protocols and with integration into implantable neural prosthetics. A portable prototype with the proposed architecture is designed and demonstrated in a bench-top test with various known biomimetic output waveforms. The prototype is also tested in vivo to deliver a complex, continuous biomimetic stimulation to a rat model of a spinal-cord injury. By delivering this unique biomimetic stimulation, the device is shown to successfully reestablish the connectivity of the spinal cord post-injury and thus restore motor outputs in the rat model.
AB - Novel neural stimulation protocols mimicking biological signals and patterns have demonstrated significant advantages as compared to traditional protocols based on uniform periodic square pulses. At the same time, the treatments for neural disorders which employ such protocols require the stimulator to be integrated into miniaturized wearable devices or implantable neural prostheses. Unfortunately, most miniaturized stimulator designs show none or very limited ability to deliver biomimetic protocols due to the architecture of their control logic, which generates the waveform. Most such designs are integrated into a single System-on-Chip (SoC) for the size reduction and the option to implement them as neural implants. But their on-chip stimulation controllers are fixed and limited in memory and computing power, preventing them from accommodating the amplitude and timing variances, and the waveform data parameters necessary to output biomimetic stimulation. To that end, a new stimulator architecture is proposed, which distributes the control logic over three component tiers – software, microcontroller firmware and digital circuits of the SoC, which is compatible with existing and future biomimetic protocols and with integration into implantable neural prosthetics. A portable prototype with the proposed architecture is designed and demonstrated in a bench-top test with various known biomimetic output waveforms. The prototype is also tested in vivo to deliver a complex, continuous biomimetic stimulation to a rat model of a spinal-cord injury. By delivering this unique biomimetic stimulation, the device is shown to successfully reestablish the connectivity of the spinal cord post-injury and thus restore motor outputs in the rat model.
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U2 - 10.3389/fnins.2021.697731
DO - 10.3389/fnins.2021.697731
M3 - Article
AN - SCOPUS:85112408392
SN - 1662-4548
VL - 15
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 697731
ER -