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	Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution
掲載誌名	正式名：IEEE Transactions on Neural Networks and Learning Systems ISSNコード：2162237X
掲載区分	国外
出版社	IEEE
巻・号・頁	PP(99),pp.1-13
総ページ数	13
著者・共著者	Sou Nobukawa, Haruhiko Nishimura, Nobuhiko Wagatsuma, Satoshi Ando, Teruya Yamanishi
発行年月	2020/08
概要	Studies of structural connectivity at the synaptic level show that in synaptic connections of the cerebral cortex, the excitatory postsynaptic potential (EPSP) in most synapses exhibits sub-mV values, while a small number of synapses exhibit large EPSPs (≳ 1.0 [mV]). This means that the distribution of EPSP fits a log-normal distribution. While not restricting structural connectivity, skewed and long-tailed distributions have been widely observed in neural activities, such as the occurrences of spiking rates and the size of a synchronously spiking population. Many studies have been modeled this long-tailed EPSP neural activity distribution; however, its causal factors remain controversial. This study focused on the long-tailed EPSP distributions and interlateral synaptic connections primarily observed in the cortical network structures, thereby having constructed a spiking neural network consistent with these features. Especially, we constructed two coupled modules of spiking neural networks with excitatory and inhibitory neural populations with a log-normal EPSP distribution. We evaluated the spiking activities for different input frequencies and with/without strong synaptic connections. These coupled modules exhibited intermittent intermodule-alternative behavior, given moderate input frequency and the existence of strong synaptic and intermodule connections. Moreover, the power analysis, multiscale entropy analysis, and surrogate data analysis revealed that the long-tailed EPSP distribution and intermodule connections enhanced the complexity of spiking activity at large temporal scales and induced nonlinear dynamics and neural activity that followed the long-tailed distribution.
DOI	10.1109/TNNLS.2020.3015208