Deterministic characteristics of spontaneous activity detected by multi-fractal analysis in a spiking neural network wit
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RESEARCH ARTICLE
Deterministic characteristics of spontaneous activity detected by multi-fractal analysis in a spiking neural network with long-tailed distributions of synaptic weights Sou Nobukawa1
•
Nobuhiko Wagatsuma2 • Haruhiko Nishimura3
Received: 12 December 2019 / Revised: 13 May 2020 / Accepted: 2 June 2020 Ó Springer Nature B.V. 2020
Abstract 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. Keywords Complexity Fluctuation Log-normal distribution Spiking neural network Spontaneous activity
Introduction Over the past decades, various types of neural activity with complex spatio-temporal patterns have been observed through studies utilizing neuroimaging modalities as well as those utilizing neural network models with high physiological neural characteristics (reviewed in Rabinovich
& Sou Nobukawa [email protected] 1
Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan
2
Faculty of Science, Department of Information Science, Toho University, 2-2-1 Miyama, Funabashi, Chiba 274-8510, Japan
3
Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, Hyogo 650-0047, Japan
et al. 2006; Garrett et al. 2011; Buzsa´ki and Mizuseki 2014; Yang and Tsai 2013; Takahashi 2013; Teramae et al. 2012; Kim and Lim 2017, 2018; Oprea et al. 2020). Among these n
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