Nonlinear dynamics and susceptible spiking of threshold-switching Hodgkin–Huxley neurons

With the development of neuromorphic computing, threshold-switching devices have been widely explored for implementing artificial neurons, but the stability of such circuits remains a critical challenge. This work systematically investigates the Hodgkin–Huxley (HH) neuron based on NbOx threshold devices from the perspective of firing instability. Through detailed modeling and simulation, various unstable firing patterns are reproduced, all of which are induced by mismatches of external factors or internal factors. Stable tonic spiking only appears within a very narrow parameter window, highlighting the poor robustness of the circuit. Therefore, significant circuit improvements are required to enhance the stability of neurons, limiting its immediate applicability in large-scale neuromorphic systems. More importantly, this work establishes a general analytical framework for investigating neuronal instability from the perspective of parameter mismatch and dynamic timing. It not only reveals the intrinsic instability of HH neurons implemented with threshold devices but also challenges the conventional assumption of their inherent robustness. Therefore, this work provides new insights for the design and evaluation of neuromorphic neuron circuits.