School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China.
J Theor Biol. 2012 Nov 21;313:98-114. doi: 10.1016/j.jtbi.2012.08.011. Epub 2012 Aug 18.
In this paper, we present a neural network system composed of two delay-coupled neural oscillators, where each of these can be regarded as the dynamical system describing the average activity of neural population. Analyzing the corresponding characteristic equation, the local stability of rest state is studied. The system exhibits the switch phenomenon between the rest state and periodic activity. Furthermore, the Hopf bifurcation is analyzed and the bifurcation curve is given in the parameters plane. The stability of the bifurcating periodic solutions and direction of the Hopf bifurcation are exhibited. Regarding time delay and coupled weight as the bifurcation parameters, the Fold-Hopf bifurcation is investigated in detail in terms of the central manifold reduction and normal form method. The neural system demonstrates the coexistence of the rest states and periodic activities in the different parameter regions. Employing the normal form of the original system, the coexistence regions are illustrated approximately near the Fold-Hopf singularity point. Finally, numerical simulations are performed to display more complex dynamics. The results illustrate that system may exhibit the rich coexistence of the different neuro-computational properties, such as the rest states, periodic activities, and quasi-periodic behavior. In particular, some periodic activities can evolve into the bursting-type behaviors with the varying time delay. It implies that the coexistence of the quasi-periodic activity and bursting-type behavior can be obtained if the suitable value of system parameter is chosen.
本文提出了一个由两个时滞耦合神经元振荡器组成的神经网络系统,其中每个振荡器都可以看作是描述神经元群体平均活动的动力系统。通过分析相应的特征方程,研究了静止状态的局部稳定性。系统表现出静止状态和周期性活动之间的转换现象。进一步分析了 Hopf 分岔,并在参数平面上给出了分岔曲线。展示了分岔周期解的稳定性和 Hopf 分岔的方向。以时滞和耦合权重为分岔参数,利用中心流形约化和规范形方法详细研究了折叠- Hopf 分岔。神经系统在不同的参数区域表现出静止状态和周期性活动的共存。利用原系统的规范形,在折叠- Hopf 奇异点附近近似地说明共存区域。最后,进行数值模拟以展示更复杂的动力学。结果表明,系统可能表现出丰富的不同神经计算特性的共存,如静止状态、周期性活动和准周期性行为。特别是,一些周期性活动随着时滞的变化可以演变成爆发型行为。这意味着如果选择合适的系统参数值,可以获得准周期性活动和爆发型行为的共存。