ワガツマ　ノブヒコ   Wagatsuma Nobuhiko   我妻　伸彦    所属   東邦大学  理学部 情報科学科    職種   講師
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Deterministic characteristics of spontaneous activity detected by multi-fractal analysis in a spiking neural network with long-tailed distributions of synaptic weights
掲載誌名	正式名：Cognitive Neurodynamics ISSNコード：18714080
掲載区分	国外
出版社	Springer
巻・号・頁	14,pp.829-836
総ページ数	8
著者・共著者	Sou Nobukawa, Nobuhiko Wagatsuma, Haruhiko Nishimura
担当区分	2nd著者
発行年月	2020/06
概要	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.
DOI	https://doi.org/10.1007/s11571-020-09605-6