Design and hardware implementation of bio-inspired neural synchronization anti-interference circuits

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.

Keywords

Bio-inspired anti-interference / Neural synchronous dynamics / HH/IZH network models / Hardware implementation

References

[1] Beaubois R , Cheslet J , Ikeuchi Y , et al., 2024. Real-time multicompartment HodgkinHuxley neuron emulation on SoC FPGA. Front Neurosci, 18: 1457774.

[2] Bossy M , Fontbona J , Olivero H , 2019. Synchronization of stochastic mean field net-works of Hodgkin-Huxley neurons with noisy channels. J Math Biol, 78 (6): 1771- 1820.

[3] Guo L , Song YH , Wu YX , et al., 2023. Anti-interference of a small-world spiking neural network against pulse noise. Appl Intell, 53 (6): 7074- 7092.

[4] Heidarpur M , Ahmadi A , Ahmadi M , 2024. The silence of the neurons: an application to enhance performance and energy efficiency. Front Neurosci, 17: 1333238.

[5] Hodgkin AL , Huxley AF , 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol, 117 (4): 500- 544.

[6] Izhikevich EM , 2003. Simple model of spiking neurons. IEEE Trans Neur Netw, 14 (6): 1569- 1572.

[7] Kazemi S , Jamali Y , 2022. Phase synchronization and measure of criticality in a network of neural mass models. Sci Rep, 12 (1): 1319.

[8] Khoshkhou M , Montakhab A , 2020. Explosive, continuous and frustrated synchronization transition in spiking Hodgkin-Huxley neural networks: the role of topology and synaptic interaction. Phys D, 405: 132399.

[9] Lin PL , Liao ZS , Chen SM , et al., 2025. Achieving neuronal dynamics with spike en-coding and spatial-temporal summation in vanadium-based threshold switching memristor for asynchronous signal integration. Nanoscale Horiz, 10 (2): 379- 387.

[10] Liu DH , Sugawara R , Hanioka S , et al., 2023. EMI mitigation using a learning-based frequency modulation carrier in PWM inverters. Proc 49^th Annual Conf of the IEEE Industrial Electronics Society, p.1- 6.

[11] Liu SH , Ma GL , Man MH , et al., 2022. Research progress of electromagnetic protection biomimetics. High Volt Eng, 48 (5): 1750- 1761 (in Chinese).

[12] Ma J , 2023. Biophysical neurons, energy, and synapse controllability: a review. J Zhejiang Univ-Sci A, 24 (2): 109- 129.

[13] Mainen ZF , Sejnowski TJ , 1995. Reliability of spike timing in neocortical neurons. Science, 268 (5216): 1503- 1506.

[14] Peng HH , Gan L , Guo X , 2024. Memristor-based spiking neural networks: cooperative development of neural network architecture/algorithms and memristors. Chip, 3 (2): 100093.

[15] Savtchenko LP , 2007. Bilateral processing in chemical synapses with electrical ‘ephap-tic’ feedback: a theoretical model. Math Biosci, 207 (1): 113- 137.

[16] Schuman CD , Kulkarni SR , Parsa M , et al., 2022. Opportunities for neuromorphic computing algorithms and applications. Nat Comput Sci, 2 (1): 10- 19.

[17] Soltani R , Benmohamed E , Ltifi H , 2024. Newman-Watts-Strogatz topology in deep echo state networks for speech emotion recognition. Eng Appl Artif Intell, 133: 108293.

[18] Takahashi S , Wada K , Ayano H , et al., 2022. Review of modeling and suppression techniques for electromagnetic interference in power conversion systems. IEEJ J Ind Appl, 11 (1): 7- 19.

[19] Wang B , Zhang XY , Wang SC , et al., 2025. Topology optimization of random memris-tors for input-aware dynamic SNN. Sci Adv, 11 (16): eads5340.

[20] Wang XQ , Yu D , Wu Y , et al., 2023. Effects of potassium channel blockage on inverse stochastic resonance in Hodgkin-Huxley neural systems. J Zhejiang Univ-Sci A, 24 (8): 735- 748.

[21] Wu XG , Gao XJ , Wang JL , et al., 2023. Advances in modeling and suppression meth-ods of EMI in power electronic converters of third-generation semiconductor de-vices. Electronics, 12 (11): 2348.

[22] Yu XH , Bao H , Chen M , et al., 2023. Energy balance via memristor synapse in MorrisLecar two-neuron network with FPGA implementation. Chaos Sol Fract, 171: 113442.

[23] Yuan L , Zhang J , Liang Z , et al., 2023. EMI challenges in modern power electronicbased converters: recent advances and mitigation techniques. Front Electron, 4: 1274258.

[24] Zhang YZ , Zhou CL , Bao H , et al., 2026. A heterogeneous cyclic Hopfield neural net-work without self-connections. J Zhejiang Univ-Sci A, 27 (3): 306- 316.

[25] Zhao YJ , Xiao LF , Liu YL , et al., 2024. Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding-free MRI. NMR Biomed, 37(7): e4956.

[26] Zhou P , Ma J , Xu Y , 2023. Phase synchronization between neurons under nonlinear coupling via hybrid synapse. Chaos Sol Fract, 169: 113238.

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