Monophasic and biphasic neurodynamics of bi-S-type locally active memristor - Wang, Xinyi; Dong, Yujiao; Wang, Guangyi; Zhou, Ziyu; Mao, Yidan; Jin, Peipei; Liang, Yan (Frontiers in Physics, 2025)
Inspired by the energy-efficient information processing of biological neural systems, this paper proposes an artificial memristive neuron to reproduce biological neuronal functions. By leveraging Chua’s unfolding theorem, we establish a bi-S-type locally active memristor mathematical model exhibiting negative differential resistance (NDR), which serve as fingerprints for local activity. A second-order neuronal circuit is constructed to emulate periodic spiking and excitability, while a third-order circuit extends functionality to chaotic oscillations and bursting behaviors. Besides, the constructed neuronal circuit generates biphasic action potential through voltage symmetry modulation, replicating bidirectional signal transmission akin to biological systems. Hardware emulation validates neurodynamics under varying stimuli from theoretical analyses, offering a unit module and theoretical reference for energy-efficient neuromorphic computing network.