Dynamics of a neuromorphic electromechanical model mimicking neuromuscular junctions

Neuromuscular junctions are vital biological interfaces between efferent nerves and muscle fibers, and their emulation remains challenging due to the lack of a clear understanding of the underlying biophysical mechanisms. To avoid complicated electromechanical coupling relationships, we propose an electromechanical model based on the Lagrange-Maxwell equations from an energy-based perspective. The model integrates a Josephson junction-based neuron with a mass-spring-damper module to mimic a biological neuromuscular junction. Numerical simulations and theoretical analysis reveal that the Josephson junction-based neuron exhibits two classes of excitability. The artificial muscle, which incorporates a nonlinear spring, undergoes an interstitial transition between kinetic and static states. Furthermore, the coupling component of the electromechanical model is identified as a meminductor, and the locally active meminductive component significantly influences the modulating vibrational patterns. We also demonstrate the emulation of biological dynamics, such as enhanced artificial muscle activity in response to increased external stimulus current. These findings provide insights into biophysical electromechanical coupling systems and suggest potential applications in bioinspired robotics and neuromorphic devices.