Frontiers in Computational Neuroscience (Jul 2020)
Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory
- Hao Wang,
- Hao Wang,
- Hao Wang,
- Hao Wang,
- Jiahui Wang,
- Jiahui Wang,
- Jiahui Wang,
- Jiahui Wang,
- Xin Yuan Thow,
- Sanghoon Lee,
- Sanghoon Lee,
- Sanghoon Lee,
- Sanghoon Lee,
- Sanghoon Lee,
- Wendy Yen Xian Peh,
- Kian Ann Ng,
- Tianyiyi He,
- Tianyiyi He,
- Tianyiyi He,
- Nitish V. Thakor,
- Chengkuo Lee,
- Chengkuo Lee,
- Chengkuo Lee,
- Chengkuo Lee,
- Chengkuo Lee
Affiliations
- Hao Wang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
- Hao Wang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Hao Wang
- Center for Intelligent Sensor and MEMS, National University of Singapore, Singapore, Singapore
- Hao Wang
- Hybrid Integrated Flexible Electronic Systems, National University of Singapore, Singapore, Singapore
- Jiahui Wang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Jiahui Wang
- Center for Intelligent Sensor and MEMS, National University of Singapore, Singapore, Singapore
- Jiahui Wang
- Hybrid Integrated Flexible Electronic Systems, National University of Singapore, Singapore, Singapore
- Jiahui Wang
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
- Xin Yuan Thow
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
- Sanghoon Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Sanghoon Lee
- Center for Intelligent Sensor and MEMS, National University of Singapore, Singapore, Singapore
- Sanghoon Lee
- Hybrid Integrated Flexible Electronic Systems, National University of Singapore, Singapore, Singapore
- Sanghoon Lee
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
- Sanghoon Lee
- Department of Robotics Engineering, Daegu Geongbuk Institute of Science and Technology (DGIST), Daegu, South Korea
- Wendy Yen Xian Peh
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
- Kian Ann Ng
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
- Tianyiyi He
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Tianyiyi He
- Center for Intelligent Sensor and MEMS, National University of Singapore, Singapore, Singapore
- Tianyiyi He
- Hybrid Integrated Flexible Electronic Systems, National University of Singapore, Singapore, Singapore
- Nitish V. Thakor
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
- Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Chengkuo Lee
- Center for Intelligent Sensor and MEMS, National University of Singapore, Singapore, Singapore
- Chengkuo Lee
- Hybrid Integrated Flexible Electronic Systems, National University of Singapore, Singapore, Singapore
- Chengkuo Lee
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
- Chengkuo Lee
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
- DOI
- https://doi.org/10.3389/fncom.2020.00050
- Journal volume & issue
-
Vol. 14
Abstract
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.
Keywords
- electric nerve stimulation
- mathematical model
- circuit-probability theory
- computational modeling
- inductor in neural circuit
