Baroni Fabiano, Burkitt Anthony N, Grayden David B
NeuroEngineering Laboratory, Dept. of Electrical & Electronic Engineering, University of Melbourne, Parkville, Victoria, Australia; Centre for Neural Engineering, University of Melbourne, Parkville, Victoria, Australia.
NeuroEngineering Laboratory, Dept. of Electrical & Electronic Engineering, University of Melbourne, Parkville, Victoria, Australia; Centre for Neural Engineering, University of Melbourne, Parkville, Victoria, Australia; Bionics Institute, East Melbourne, Victoria, Australia.
PLoS Comput Biol. 2014 May 1;10(5):e1003574. doi: 10.1371/journal.pcbi.1003574. eCollection 2014 May.
High-frequency oscillations (above 30 Hz) have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF) or Generalized Integrate-and-Fire (GIF) neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i) the firing rate response to the noisy background input, ii) the membrane potential distribution, and iii) the shape of Inhibitory Post-Synaptic Potentials (IPSPs). For hyperpolarizing inhibition, the GIF IPSP profile (factor iii)) exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i) and ii), respectively), which tend to decrease synchrony. If inhibition is shunting instead of hyperpolarizing, post-inhibitory rebound is not elicited and factors i) and ii) dominate, yielding lower synchrony in GIF networks than in IF networks.
在感觉和高级脑区已观察到高频振荡(高于30Hz),并且被认为是功能性神经元激活的一个普遍标志。中间神经元网络中的快速抑制被认为是产生高频振荡的一般机制。某些类型的中间神经元表现出阈下振荡,但这种内在神经元特性对群体节律的影响尚未完全了解。我们研究内在阻尼阈下振荡在集体高频振荡出现中的影响,并阐明这一现象背后的动力学机制。我们模拟了由积分发放(IF)神经元或广义积分发放(GIF)神经元组成的神经元网络。IF模型显示纯粹的被动阈下动力学,而GIF模型表现出阈下阻尼振荡。单个神经元接收由其邻居的发放活动介导的抑制性突触电流以及有噪声的突触轰击,并以低于群体频率的较低速率不规则发放。我们确定了影响单神经元特性对抑制介导的同步化影响的三个因素:i)对有噪声背景输入的发放率响应,ii)膜电位分布,以及iii)抑制性突触后电位(IPSP)的形状。对于超极化抑制,GIF的IPSP轮廓(因素iii))表现出抑制后反弹,这会在细胞间诱导一个连贯的、由发放介导的去极化,极大地促进同步振荡。这种效应主导了网络动力学,因此GIF网络比IF网络表现出更强的振荡。然而,GIF神经元中的恢复电流降低了发放率并缩小了膜电位分布(分别为因素i)和ii)),这往往会降低同步性。如果抑制是分流性而非超极化性的,则不会引发抑制后反弹,因素i)和ii)起主导作用,导致GIF网络中的同步性低于IF网络。