Ghosh Subrata, Mondal Argha, Ji Peng, Mishra Arindam, Dana Syamal K, Antonopoulos Chris G, Hens Chittaranjan
Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata, India.
The Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Front Comput Neurosci. 2020 Jun 8;14:49. doi: 10.3389/fncom.2020.00049. eCollection 2020.
In this paper, we focus on the emergence of diverse neuronal oscillations arising in a mixed population of neurons with different excitability properties. These properties produce mixed mode oscillations (MMOs) characterized by the combination of large amplitudes and alternate subthreshold or small amplitude oscillations. Considering the biophysically plausible, Izhikevich neuron model, we demonstrate that various MMOs, including MMBOs (mixed mode bursting oscillations) and synchronized tonic spiking appear in a randomly connected network of neurons, where a fraction of them is in a quiescent (silent) state and the rest in self-oscillatory (firing) states. We show that MMOs and other patterns of neural activity depend on the number of oscillatory neighbors of quiescent nodes and on electrical coupling strengths. Our results are verified by constructing a reduced-order network model and supported by systematic bifurcation diagrams as well as for a small-world network. Our results suggest that, for weak couplings, MMOs appear due to the de-synchronization of a large number of quiescent neurons in the networks. The quiescent neurons together with the firing neurons produce high frequency oscillations and bursting activity. The overarching goal is to uncover a favorable network architecture and suitable parameter spaces where Izhikevich model neurons generate diverse responses ranging from MMOs to tonic spiking.
在本文中,我们关注在具有不同兴奋性特性的混合神经元群体中出现的多种神经元振荡。这些特性产生了混合模式振荡(MMO),其特征是大振幅与交替的阈下或小振幅振荡相结合。考虑到具有生物物理合理性的艾克米维奇神经元模型,我们证明了各种MMO,包括混合模式爆发振荡(MMBO)和同步紧张性放电,出现在一个随机连接的神经元网络中,其中一部分处于静止(沉默)状态,其余处于自振荡(放电)状态。我们表明,MMO和其他神经活动模式取决于静止节点的振荡邻居数量以及电耦合强度。我们的结果通过构建一个降阶网络模型得到验证,并得到系统分岔图以及一个小世界网络的支持。我们的结果表明,对于弱耦合,MMO的出现是由于网络中大量静止神经元的去同步化。静止神经元与放电神经元一起产生高频振荡和爆发活动。总体目标是揭示一个有利的网络架构和合适的参数空间,在其中艾克米维奇模型神经元能产生从MMO到紧张性放电的各种不同反应。