Firing Pattern and Electrical Modulation in a Dual-Channel Neural Circuit With Locally Active Memristors

Leveraging the ability of locally active memristors to generate and flexibly modulate neuronal firing patterns with simple topologies, this paper proposes a dual-channel memristive neural circuit for scalable neuromorphic implementation. The proposed design employs two structurally identical branches, each incorporating a locally active memristor, enabling on-demand control of firing dynamics within a unified hardware framework. We systematically analyze the stability distribution of equilibrium points and investigate the underlying firing patterns under various stimulus conditions through numerical bifurcation analysis. The results demonstrate that the proposed neural circuit topology supports controllable transitions among resting, spiking, bursting, and chaotic states under undriven, DC-driven, and pulse-triggered conditions. These transitions are achieved through electrical modulation of key circuit parameters, namely the memristor bias voltages, external stimulation, and coupling resistance. PCB-level experimental measurements validate the feasibility of the theoretical and simulation analyses. Overall, this work provides fundamental guidance for designing LAM-based neural circuits and contributes to the development of essential building blocks for neuromorphic engineering.