Li Xuening, Yu Dong, Yang Lijian, Fu Ziying, Jia Ya
Department of Physics, Central China Normal University, Wuhan, 430079 China.
School of Biology, Central China Normal University, Wuhan, 430079 China.
Cogn Neurodyn. 2024 Apr;18(2):685-700. doi: 10.1007/s11571-023-10021-9. Epub 2023 Nov 2.
Energy absorption and consumption are essential for the activity of single neurons and neuronal networks. The synchronization mode transition and energy dependence in a delay-coupled FitzHugh-Nagumo (FHN) neuronal system driven by chaotic activity are investigated in this paper. With the change of chaotic current intensity, it was found that the synchronization mode of coupled neurons undergoes synchronous state, transition state, anti-phase state, alternating asynchronous and anti-phase state, and chaotic current-induced chaotic state. The Hamiltonian energy is much dependent on the synchronization mode of coupled neurons. The synchronization mode and the Hamiltonian energy of coupled neurons can be modulated by chaotic current intensity, coupling strength and time delay. The introduction of the time delay induces the system to become bistable state. Chaotic current as an external force induced transitions between the synchronous and anti-phase states. Coupling strength is an intrinsic property of the system and can change the properties of the bistable state. Furthermore, the synchronous and anti-phase states appear intermittently with the increasing of time delay. A chained neuronal network is used to prove that the synchronization mode transition of the system of multiple neurons is similar to the two neurons. The results of this paper might help one to understand the intrinsic energy alteration mechanisms of neuronal synchronization.
能量吸收和消耗对于单个神经元及神经元网络的活动至关重要。本文研究了由混沌活动驱动的延迟耦合FitzHugh-Nagumo(FHN)神经元系统中的同步模式转变和能量依赖性。随着混沌电流强度的变化,发现耦合神经元的同步模式经历同步状态、过渡状态、反相状态、交替异步和反相状态以及混沌电流诱导的混沌状态。哈密顿能量很大程度上依赖于耦合神经元的同步模式。耦合神经元的同步模式和哈密顿能量可通过混沌电流强度、耦合强度和时间延迟进行调制。时间延迟的引入使系统变为双稳态。混沌电流作为外力诱导同步状态和反相状态之间的转变。耦合强度是系统的固有属性,可改变双稳态的性质。此外,随着时间延迟的增加,同步状态和反相状态间歇性出现。使用链式神经元网络证明多个神经元系统的同步模式转变与两个神经元的相似。本文的结果可能有助于人们理解神经元同步的内在能量改变机制。