Nobukawa Sou, Wagatsuma Nobuhiko, Nishimura Haruhiko
Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016 Japan.
Faculty of Science, Department of Information Science, Toho University, 2-2-1 Miyama, Funabashi, Chiba 274-8510 Japan.
Cogn Neurodyn. 2020 Dec;14(6):829-836. doi: 10.1007/s11571-020-09605-6. Epub 2020 Jun 24.
Cortical neural networks maintain autonomous electrical activity called spontaneous activity that represents the brain's dynamic internal state even in the absence of sensory stimuli. The spatio-temporal complexity of spontaneous activity is strongly related to perceptual, learning, and cognitive brain functions; multi-fractal analysis can be utilized to evaluate the complexity of spontaneous activity. Recent studies have shown that the deterministic dynamic behavior of spontaneous activity especially reflects the topological neural network characteristics and changes of neural network structures. However, it remains unclear whether multi-fractal analysis, recently widely utilized for neural activity, is effective for detecting the complexity of the deterministic dynamic process. To verify this point, we focused on the log-normal distribution of excitatory postsynaptic potentials (EPSPs) to evaluate the multi-fractality of spontaneous activity in a spiking neural network with a log-normal distribution of EPSPs. We found that the spiking activities exhibited multi-fractal characteristics. Moreover, to investigate the presence of a deterministic process in the spiking activity, we conducted a surrogate data analysis against the time-series of spiking activity. The results showed that the spontaneous spiking activity included the deterministic dynamic behavior. Overall, the combination of multi-fractal analysis and surrogate data analysis can detect deterministic complex neural activity. The multi-fractal analysis of neural activity used in this study could be widely utilized for brain modeling and evaluation methods for signals obtained by neuroimaging modalities.
皮层神经网络维持着一种称为自发活动的自主电活动，即使在没有感觉刺激的情况下，这种活动也代表着大脑的动态内部状态。自发活动的时空复杂性与感知、学习和认知脑功能密切相关；多重分形分析可用于评估自发活动的复杂性。最近的研究表明，自发活动的确定性动态行为尤其反映了拓扑神经网络特征和神经网络结构的变化。然而，最近广泛用于神经活动的多重分形分析是否能有效检测确定性动态过程的复杂性仍不清楚。为了验证这一点，我们聚焦于兴奋性突触后电位（EPSP）的对数正态分布，以评估具有EPSP对数正态分布的脉冲神经网络中自发活动的多重分形性。我们发现脉冲活动表现出多重分形特征。此外，为了研究脉冲活动中确定性过程的存在，我们对脉冲活动的时间序列进行了替代数据分析。结果表明，自发脉冲活动包含确定性动态行为。总体而言，多重分形分析和替代数据分析相结合可以检测确定性复杂神经活动。本研究中使用的神经活动多重分形分析可广泛应用于脑建模以及神经成像模态获得的信号评估方法。
