Kim Sang-Yoon, Lim Woochang
Computational Neuroscience Lab., Department of Science Education, Daegu National University of Education, Daegu, 705-115 Korea.
Cogn Neurodyn. 2015 Aug;9(4):411-21. doi: 10.1007/s11571-015-9334-4. Epub 2015 Mar 14.
We are interested in characterization of synchronization transitions of bursting neurons in the frequency domain. Instantaneous population firing rate (IPFR) [Formula: see text], which is directly obtained from the raster plot of neural spikes, is often used as a realistic collective quantity describing population activities in both the computational and the experimental neuroscience. For the case of spiking neurons, a realistic time-domain order parameter, based on [Formula: see text], was introduced in our recent work to characterize the spike synchronization transition. Unlike the case of spiking neurons, the IPFR [Formula: see text] of bursting neurons exhibits population behaviors with both the slow bursting and the fast spiking timescales. For our aim, we decompose the IPFR [Formula: see text] into the instantaneous population bursting rate [Formula: see text] (describing the bursting behavior) and the instantaneous population spike rate [Formula: see text] (describing the spiking behavior) via frequency filtering, and extend the realistic order parameter to the case of bursting neurons. Thus, we develop the frequency-domain bursting and spiking order parameters which are just the bursting and spiking "coherence factors" [Formula: see text] and [Formula: see text] of the bursting and spiking peaks in the power spectral densities of [Formula: see text] and [Formula: see text] (i.e., "signal to noise" ratio of the spectral peak height and its relative width). Through calculation of [Formula: see text] and [Formula: see text], we obtain the bursting and spiking thresholds beyond which the burst and spike synchronizations break up, respectively. Consequently, it is shown in explicit examples that the frequency-domain bursting and spiking order parameters may be usefully used for characterization of the bursting and the spiking transitions, respectively.
我们感兴趣的是在频域中对爆发性神经元的同步转变进行表征。瞬时群体发放率(IPFR)[公式:见正文],它直接从神经尖峰的光栅图中获得,在计算神经科学和实验神经科学中,常被用作描述群体活动的一个现实的集体量。对于发放脉冲的神经元的情况,在我们最近的工作中引入了一个基于[公式:见正文]的现实时域序参量,以表征尖峰同步转变。与发放脉冲的神经元的情况不同,爆发性神经元的IPFR [公式:见正文]表现出具有慢爆发和快发放时间尺度的群体行为。为了实现我们的目标,我们通过频率滤波将IPFR [公式:见正文]分解为瞬时群体爆发率[公式:见正文](描述爆发行为)和瞬时群体发放率[公式:见正文](描述发放行为),并将现实序参量扩展到爆发性神经元的情况。因此,我们开发了频域爆发和发放序参量,它们恰好是[公式:见正文]和[公式:见正文]的功率谱密度中爆发和发放峰值的爆发和发放“相干因子”[公式:见正文]和[公式:见正文](即频谱峰值高度与其相对宽度的“信噪比”)。通过计算[公式:见正文]和[公式:见正文],我们分别得到了爆发和发放阈值,超过这些阈值,爆发和发放同步就会分别瓦解。因此,在明确的例子中表明,频域爆发和发放序参量可分别有效地用于表征爆发和发放转变。