听觉神经元在混沌活动诱导下的复杂动态行为转换

IF 2 4区 生物学 Q2 BIOLOGY
Guodong Huang, Shu Zhou, Rui Zhu, Yunhai Wang, Yuan Chai
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引用次数: 0

摘要

混沌序列因其高度随机性而被广泛应用于安全通信领域。混沌共振(CR)是指系统对混沌活动引起的微弱信号的共振响应,但其实际应用仍然有限。本研究通过模拟听觉神经元的生理活动,并考虑混沌活动和声音信号的联合刺激,设计了一个简化的 FitzHugh-Nagumo (FHN)听觉神经元模型。研究发现,神经元动态取决于外部声音刺激和混沌电流强度。混沌电流通过 CR 在神经元输出序列中诱发尖峰,尖峰随着电流强度的增加而变得更加频繁,最终导致混沌状态,而与初始状态无关。然而,这种混沌序列初始值的敏感性会随着混沌电流激励系统的变化而变化。在某些条件下,注入混沌电流可以降低系统的平均哈密顿能量。通过测量生成序列的复杂性,我们发现混沌电流的加入可以增强原始序列的复杂性,而且增强能力随着强度的增加而增强。这为增强原始混沌序列的复杂性提供了一种新方法。此外,不同的混沌电流可以诱发不同的混沌序列,其增强原始序列复杂性的能力也各不相同。我们希望我们的工作能为安全通信做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Complex dynamic behavioral transitions in auditory neurons induced by chaotic activity
Chaotic sequences are widely used in secure communication due to their high randomness. Chaotic resonance (CR) refers to the resonant response of a system to weak signals induced by chaotic activity, but its practical application remains limited. This study designs a simplified FitzHugh-Nagumo (FHN) auditory neuron model by simulating the physiological activities of auditory neurons and considering the combined stimulation of chaotic activity and sound signals. It is found that the neuron dynamics depend on both external sound stimuli and chaotic current intensity. Chaotic currents induce spikes in the neuron output sequence through CR, and the spikes become more frequent with increasing current intensity, eventually leading to a chaotic state regardless of the initial state. However, the sensitivity of the initial value of this chaotic sequence shifts to the chaotic current excitation system. The injection of chaotic currents can reduce the system's average Hamiltonian energy under certain conditions. By measuring the complexity of the generated sequences, we find that the addition of chaotic currents can enhance the complexity of the original sequences, and the enhancement ability increases with the intensity. This provides a new approach to enhance the complexity of original chaotic sequences. Moreover, different chaotic currents can induce different chaotic sequences with varying abilities to enhance the complexity of the original sequences. We hope our work can contribute to secure communication.
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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