Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory

• Main Authors: Hao Wang, Jiahui Wang, Xin Yuan Thow, Sanghoon Lee, Wendy Yen Xian Peh, Kian Ann Ng, Tianyiyi He, Nitish V. Thakor, Chengkuo Lee
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-07-01
• Series: Frontiers in Computational Neuroscience
• Subjects: electric nerve stimulation; mathematical model; circuit-probability theory; computational modeling; inductor in neural circuit
• Online Access: https://www.frontiersin.org/article/10.3389/fncom.2020.00050/full

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.