TY - JOUR
T1 - Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory
AU - Wang, Hao
AU - Wang, Jiahui
AU - Thow, Xin Yuan
AU - Lee, Sanghoon
AU - Peh, Wendy Yen Xian
AU - Ng, Kian Ann
AU - He, Tianyiyi
AU - Thakor, Nitish V.
AU - Lee, Chengkuo
N1 - Funding Information:
We would like to thank for the experiment setup support from Han Wu, Shih Chiang Liu, Astrid, Shuhao Lu, Li Jing Ong, and Dian Sheng Wong. We also would like to thank for the animal experiment support from Gammad Gil Gerald Lasam. We have our special acknowledgment to James T. Fulton for his pioneer research of neuroscience published on the Internet. Funding. This work was supported by grants from the National Research Foundation Competitive research programme (NRF-CRP) Peripheral Nerve Prostheses: A Paradigm Shift in Restoring Dexterous Limb Function (NRF-CRP10-2012-01), National Research Foundation Competitive research programme (NRF-CRP) Energy Harvesting Solutions for Biosensors (R-263-000-A27-281), National Research Foundation Competitive research programme (NRF-CRP) Piezoelectric Photonics Using CMOS Compatible AlN Technology for Enabling the Next Generation Photonics ICs and Nanosensors (R-263-000-C24-281), Faculty Research Committee (FRC) Thermoelectric Power Generator (TEG) Based Self-Powered ECG Plaster—System Integration (Part 3) (R-263-000-B56-112), and HIFES Seed Funding Hybrid Integration of Flexible Power Source and Pressure Sensors (R-263-501-012-133).
Publisher Copyright:
© Copyright © 2020 Wang, Wang, Thow, Lee, Peh, Ng, He, Thakor and Lee.
PY - 2020/7/10
Y1 - 2020/7/10
N2 - Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new theory, named the Circuit-Probability theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be theoretically explained, derived, and amended using our theory. Furthermore, this theory can explain the complex non-linear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a parallel RLC circuit first, and then calculate the excitation probability stochastically.
AB - Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new theory, named the Circuit-Probability theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be theoretically explained, derived, and amended using our theory. Furthermore, this theory can explain the complex non-linear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a parallel RLC circuit first, and then calculate the excitation probability stochastically.
KW - circuit-probability theory
KW - computational modeling
KW - electric nerve stimulation
KW - inductor in neural circuit
KW - mathematical model
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U2 - 10.3389/fncom.2020.00050
DO - 10.3389/fncom.2020.00050
M3 - Article
C2 - 32754023
AN - SCOPUS:85088525925
SN - 1662-5188
VL - 14
JO - Frontiers in Computational Neuroscience
JF - Frontiers in Computational Neuroscience
M1 - 50
ER -