Abstract: Conventional circuit systems typically exhibit limited capability in suppressing disturbances with strong temporal structures. This study proposes a bio-inspired anti-interference framework based on neural synchronization mechanisms. Hodgkin–Huxley (HH) and Izhikevich (IZH) networks with different topologies are compared in terms of synchronization stability, energy deviation, and hardware feasibility under external interference. The proposed framework integrates the neuron models and topological structures of neural networks to establish a unified evaluation system for bio-inspired anti-interference circuits. The simulation results demonstrate that the HH network exhibits stronger robustness under strong coupling, maintaining a synchronization index near 1.0 and a lower relative energy deviation than the IZH network. The small-world topology achieves the best disturbance tolerance, whereas the chain topology exhibits the weakest robustness for both models. In addition, hardware implementation demonstrates that the IZH network significantly reduces lookup table consumption, and both the HH and IZH circuits maintain rhythmic firing and waveform integrity under 20 mV square-wave interference. These results indicate that neural synchronization mechanisms provide an effective and practically implementable mechanism for adaptive anti-interference circuit design.

仿生神经同步抗干扰电路设计与硬件实现

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