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<p>Synchronization of neuronal populations in complex networks is essential to understand information coding and cognitive function, where the energy regulates the firing pattern of neurons and determines the communication between them. In this paper, the small-world (WS) neural network and scale-free (SF) neural network are constructed by the functional memristive FitzHugh–Nagumo neural circuit coupled by the energy balance strategy. The individual neuron presents periodic firing with high energy and chaotic patterns maintaining the relatively lower energy under different external stimuli. The network built by the similar periodic-type neurons can realize complete synchronization and energy balance under lower coupling intensity than the chaotic counterpart. Particularly, the lower saturated coupling strength and more pronounced energy accumulation are observed in SF network compared to the WS network. It is uncovered that the synchronization of neurons in heterogeneous network is determined by the energy instead of membrane potentials, and the energy synchronization factor is persuasive to measure synchronization. Additionally, the SF network loses efficacy in maintaining synchronization stability, which is determined by the nodes of high degrees. Our results provide new guidelines to build efficient neural networks and lead to a better understanding of synchronization mechanisms.</p>

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Synchronization of complex networks with synapse regulated by energy difference

  • 期刊论文

  • 发表日期：

    2025年1月6日

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  • Ying Xie,
  • Xuening Li,
  • Xueqin Wang,
  • Zhiqiu Ye,
  • Xueyan Hu,
  • Yipeng Hu,
  • Ya Jia

摘要

Synchronization of neuronal populations in complex networks is essential to understand information coding and cognitive function, where the energy regulates the firing pattern of neurons and determines the communication between them. In this paper, the small-world (WS) neural network and scale-free (SF) neural network are constructed by the functional memristive FitzHugh–Nagumo neural circuit coupled by the energy balance strategy. The individual neuron presents periodic firing with high energy and chaotic patterns maintaining the relatively lower energy under different external stimuli. The network built by the similar periodic-type neurons can realize complete synchronization and energy balance under lower coupling intensity than the chaotic counterpart. Particularly, the lower saturated coupling strength and more pronounced energy accumulation are observed in SF network compared to the WS network. It is uncovered that the synchronization of neurons in heterogeneous network is determined by the energy instead of membrane potentials, and the energy synchronization factor is persuasive to measure synchronization. Additionally, the SF network loses efficacy in maintaining synchronization stability, which is determined by the nodes of high degrees. Our results provide new guidelines to build efficient neural networks and lead to a better understanding of synchronization mechanisms.