Temperature effects on the neuronal dynamics and Hamilton energy

IF 5.6 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Chaos Solitons & Fractals Pub Date : 2025-06-01 Epub Date: 2025-03-19 DOI:10.1016/j.chaos.2025.116325
Ying Xie, Zhiqiu Ye, Xueqin Wang, Ya Jia, Xueyan Hu, Xuening Li
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Abstract

Neurons require suitable temperature to function, and temperature is a key factor influencing neuronal responses and energy dynamics. However, it is still unclear how temperature intensity and duration impose effect on the neuronal dynamics. To fill this gap, a thermistor is introduced into a memristive FitzHugh-Nagumo neural circuit for temperature perception (T_mFHN). The results reveal that the temperature stimulation induce different energy coding mechanisms in neurons, and the bursting-type neuron maintains higher energy levels than chaotic-type neurons. Notably, the energy of bursting-type neurons is higher than the energy maintained by chaotic-type neurons. Under low-temperature-scale, energy remains stable regardless of the duration of the temperature stimulus, but it increases without temperature in chaotic-type neurons. Additionally, temperature fluctuations shorten the setup time for synchronization and energy balance in the coupled systems. Remarkably, a brief duration of temperature stimulation induces synchronization, which remains stable and robust even without temperature stimulation under certain conditions. These findings provide valuable insights into how temperature influences neuronal dynamics and energy properties, and it offers guidance for designing neural networks with optimized temperature intensities and durations.
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温度对神经元动力学和汉密尔顿能量的影响
神经元需要适当的温度才能发挥功能,而温度是影响神经元反应和能量动态的关键因素。然而,温度强度和持续时间对神经元动力学的影响尚不清楚。为了填补这一空白,将热敏电阻引入记忆性FitzHugh-Nagumo温度感知神经回路(T_mFHN)。结果表明,温度刺激诱导神经元的能量编码机制不同,突发型神经元比混沌型神经元维持更高的能量水平。值得注意的是,突发型神经元的能量高于混沌型神经元所维持的能量。在低温尺度下,无论温度刺激持续多长时间,能量都保持稳定,但在混沌型神经元中,能量在没有温度的情况下增加。此外,温度波动缩短了耦合系统同步和能量平衡的建立时间。值得注意的是,短时间的温度刺激诱导了同步,即使在没有温度刺激的情况下,在某些条件下也保持稳定和强健。这些发现为了解温度如何影响神经元动力学和能量特性提供了有价值的见解,并为设计具有优化温度强度和持续时间的神经网络提供了指导。
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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