Ying Xie, Zhiqiu Ye, Xueqin Wang, Ya Jia, Xueyan Hu, Xuening Li
{"title":"Temperature effects on the neuronal dynamics and Hamilton energy","authors":"Ying Xie, Zhiqiu Ye, Xueqin Wang, Ya Jia, Xueyan Hu, Xuening Li","doi":"10.1016/j.chaos.2025.116325","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"195 ","pages":"Article 116325"},"PeriodicalIF":5.3000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos Solitons & Fractals","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960077925003388","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
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.