Top

Cognitive Neurodynamics

Published in:

24-06-2020 | Research Article

Deterministic characteristics of spontaneous activity detected by multi-fractal analysis in a spiking neural network with long-tailed distributions of synaptic weights

Authors: Sou Nobukawa, Nobuhiko Wagatsuma, Haruhiko Nishimura

Published in: Cognitive Neurodynamics | Issue 6/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article EEG-based emotion recognition using 4D convolutional recurrent neural network

next article Probabilistically segregated neural circuits and subcritical linguistics

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Adeli H, Ghosh-Dastidar S, Dadmehr N (2005a) Alzheimer’s disease and models of computation: imaging, classification, and neural models. J Alzheimers Dis 7(3):187–199PubMed

Adeli H, Ghosh-Dastidar S, Dadmehr N (2005b) Alzheimer’s disease: models of computation and analysis of EEGs. Clin EEG Neurosci 36(3):131–140PubMed

Adeli H, Ghosh-Dastidar S, Dadmehr N (2008) A spatio-temporal wavelet-chaos methodology for eeg-based diagnosis of Alzheimer’s disease. Neurosci Lett 444(2):190–194PubMed

Bellec G, Salaj D, Subramoney A, Legenstein R, Maass W (2018) Long short-term memory and learning-to-learn in networks of spiking neurons. In: Advances in neural information processing systems, pp 787–797

Bonzon P (2017) Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications. Cogn Neurodyn 11(4):327–353PubMedPubMedCentral

Brookes MJ, Woolrich M, Luckhoo H, Price D, Hale JR, Stephenson MC, Barnes GR, Smith SM, Morris PG (2011) Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc Nat Acad Sci 108(40):16783–16788PubMed

Buzsáki G, Mizuseki K (2014) The log-dynamic brain: how skewed distributions affect network operations. Nat Rev Neurosci 15(4):264–278PubMedPubMedCentral

da Silva FL (2013) EEG and MEG: relevance to neuroscience. Neuron 80(5):1112–1128

Destexhe A (2009) Self-sustained asynchronous irregular states and up–down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons. J Comput Neurosci 27(3):493PubMed

Easwaramoorthy D, Uthayakumar R (2010) Analysis of biomedical EEG signals using wavelet transforms and multifractal analysis. In: 2010 international conference on communication control and computing technologies, IEEE, pp 544–549

Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700PubMed

Garrett DD, Kovacevic N, McIntosh AR, Grady CL (2011) The importance of being variable. J Neurosci 31(12):4496–4503PubMedPubMedCentral

Gautama T, Mandic DP, Van Hulle MM (2003) Indications of nonlinear structures in brain electrical activity. Phys Rev E 67(4):046204

Goodman DF, Stimberg M, Yger P, Brette R (2014) Brian 2: neural simulations on a variety of computational hardware. BMC Neurosci 15(1):P199PubMedCentral

Guo D, Li C (2010) Self-sustained irregular activity in 2-D small-world networks of excitatory and inhibitory neurons. IEEE Trans Neural Netw 21(6):895–905PubMed

Hahn G, Ponce-Alvarez A, Monier C, Benvenuti G, Kumar A, Chavane F, Deco G, Frégnac Y (2017) Spontaneous cortical activity is transiently poised close to criticality. PLoS Comput Biol 13(5):e1005543PubMedPubMedCentral

Hasegawa C, Takahashi T, Yoshimura Y, Ikeda T, Saito DN, Kumazaki H, Minabe Y, Kikuchi M (2018) Developmental trajectory of infant brain signal variability: a longitudinal pilot study. Front Neurosci 12:566PubMedPubMedCentral

Jaffard S, Lashermes B, Abry P (2006) Wavelet leaders in multifractal analysis. In: Wavelet analysis and applications, Springer, pp 201–246

Kanamaru T (2017) Chaotic pattern alternations can reproduce properties of dominance durations in multistable perception. Neural Comput 29(6):1696–1720PubMed

Kantz H, Schreiber T (2004) Nonlinear time series analysis, vol 7. Cambridge University Press, Cambridge

Kheradpisheh SR, Ganjtabesh M, Thorpe SJ, Masquelier T (2018) STDP-based spiking deep convolutional neural networks for object recognition. Neural Netw 99:56–67PubMed

Kim SY, Lim W (2017) Dynamical responses to external stimuli for both cases of excitatory and inhibitory synchronization in a complex neuronal network. Cogn Neurodyn 11(5):395–413PubMedPubMedCentral

Kim SY, Lim W (2018) Effect of spike-timing-dependent plasticity on stochastic burst synchronization in a scale-free neuronal network. Cogn Neurodyn 12(3):315–342PubMedPubMedCentral

Klimesch W, Sauseng P, Hanslmayr S, Gruber W, Freunberger R (2007) Event-related phase reorganization may explain evoked neural dynamics. Neurosci Biobehav Rev 31(7):1003–1016PubMed

Kulkarni SR, Rajendran B (2018) Spiking neural networks for handwritten digit recognition—supervised learning and network optimization. Neural Netw 103:118–127PubMed

La Rocca D, Zilber N, Abry P, van Wassenhove V, Ciuciu P (2018) Self-similarity and multifractality in human brain activity: a wavelet-based analysis of scale-free brain dynamics. J Neurosci Methods 309:175–187PubMed

Lee JH, Delbruck T, Pfeiffer M (2016) Training deep spiking neural networks using backpropagation. Front Neurosci 10:508PubMedPubMedCentral

Lin X, Wang X, Hao Z (2017) Supervised learning in multilayer spiking neural networks with inner products of spike trains. Neurocomputing 237:59–70

Lin Z, Ma D, Meng J, Chen L (2018) Relative ordering learning in spiking neural network for pattern recognition. Neurocomputing 275:94–106

Maksimenko VA, Pavlov A, Runnova AE, Nedaivozov V, Grubov V, Koronovslii A, Pchelintseva SV, Pitsik E, Pisarchik AN, Hramov AE (2018) Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects. Nonlinear Dyn 91(4):2803–2817

McCormick DA (1999) Spontaneous activity: signal or noise? Science 285(5427):541–543PubMed

Mizuno T, Takahashi T, Cho RY, Kikuchi M, Murata T, Takahashi K, Wada Y (2010) Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clin Neurophysiol 121(9):1438–1446PubMedPubMedCentral

Mozafari M, Kheradpisheh SR, Masquelier T, Nowzari-Dalini A, Ganjtabesh M (2018) First-spike-based visual categorization using reward-modulated STDP. IEEE Trans Neural Netw Learn Syst 29:6178–6190PubMed

Nobukawa S, Aiura H, Yoshida H, Nishimura H, Yamanishi T (2018a) Temporal fluctuation in spontaneous activity of a spiking neural network with long-tailed synaptic weights. In: Proceedings of 2018 international symposium on nonlinear theory and its applications (NOLTA 2018), IEICE, pp 375–378

Nobukawa S, Nishimura H, Yamanishi T (2018b) Skewed and long-tailed distributions of spiking activity in coupled network modules with log-normal synaptic weight distribution. In: International conference on neural information processing, Springer, pp 535–544

Nobukawa S, Kikuchi M, Takahashi T (2019a) Changes in functional connectivity dynamics with aging: a dynamical phase synchronization approach. NeuroImage 188:357–368PubMed

Nobukawa S, Nishimura H, Yamanishi T (2019b) Pattern classification by spiking neural networks combining self-organized and reward-related spike-timing-dependent plasticity. J Artif Intell Soft Comput Res 9(4):283–291

Nobukawa S, Nishimura H, Yamanishi T (2019c) Temporal-specific complexity of spiking patterns in spontaneous activity induced by a dual complex network structure. Sci Rep 9(1):1–12

Nobukawa S, Yamanishi T, Nishimura H, Wada Y, Kikuchi M, Takahashi T (2019d) Atypical temporal-scale-specific fractal changes in Alzheimer’s disease EEG and their relevance to cognitive decline. Cogn Neurodyn 13(1):1–11PubMed

Nobukawa S, Yamanishi T, Kasakawa S, Nishimura H, Kikuchi M, Takahashi T (2020) Classification methods based on complexity and synchronization of electroencephalography signals in Alzheimer’s disease. Front Psychiatry 11:255PubMedPubMedCentral

Nurujjaman M, Narayanan R, Iyengar AS (2009) Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients. Nonlinear Biomed Phys 3(1):6PubMedPubMedCentral

Okazaki R, Takahashi T, Ueno K, Takahashi K, Ishitobi M, Kikuchi M, Higashima M, Wada Y (2015) Changes in EEG complexity with electroconvulsive therapy in a patient with autism spectrum disorders: a multiscale entropy approach. Front Hum Neurosci 9:106PubMedPubMedCentral

Oprea L, Pack CC, Khadra A (2020) Machine classification of spatiotemporal patterns: automated parameter search in a rebounding spiking network. Cogn Neurodyn 14:267–280PubMed

Rabinovich MI, Varona P, Selverston AI, Abarbanel HD (2006) Dynamical principles in neuroscience. Rev Mod Phys 78(4):1213

Sakata S, Harris KD (2009) Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex. Neuron 64(3):404–418PubMedPubMedCentral

Samura T, Ikegaya Y, Sato YD (2015) A neural network model of reliably optimized spike transmission. Cogn Neurodyn 9(3):265–277PubMedPubMedCentral

Schreiber T, Schmitz A (1996) Improved surrogate data for nonlinearity tests. Phys Rev Lett 77(4):635PubMed

Stam CJ (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116(10):2266–2301PubMed

Takahashi T (2013) Complexity of spontaneous brain activity in mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 45:258–266PubMed

Takahashi T, Cho RY, Mizuno T, Kikuchi M, Murata T, Takahashi K, Wada Y (2010) Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 51(1):173–182PubMedPubMedCentral

Takahashi T, Yoshimura Y, Hiraishi H, Hasegawa C, Munesue T, Higashida H, Minabe Y, Kikuchi M (2016) Enhanced brain signal variability in children with autism spectrum disorder during early childhood. Hum Brain Mapp 37(3):1038–1050PubMed

Tavanaei A, Kirby Z, Maida AS (2018a) Training spiking ConvNets by STDP and gradient descent. In: 2018 international joint conference on neural networks (IJCNN), IEEE, pp 1–8

Tavanaei A, Masquelier T, Maida A (2018b) Representation learning using event-based STDP. Neural Netw 105:294–303PubMed

Teplan M et al (2002) Fundamentals of EEG measurement. Meas Sci Rev 2(2):1–11

Teramae J, Tsubo Y, Fukai T (2012) Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links. Sci Rep 2:1–6

Tetko IV, Villa AE (2001) A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings. J Neurosci Methods 105(1):15–24PubMed

Theiler J, Eubank S, Longtin A, Galdrikian B, Doyne Farmer J (1992) Testing for nonlinearity in time series: the method of surrogate data. Physica D 58:77–94

Uthayakumar R, Easwaramoorthy D (2013) Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform. Fractals 21(02):1350011

Van de Ville D, Britz J, Michel CM (2010) EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Nat Acad Sci 107(42):18179–18184PubMed

Vogels TP, Abbott LF (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25(46):10786–10795PubMedPubMedCentral

Wendt H, Abry P (2007) Multifractality tests using bootstrapped wavelet leaders. IEEE Trans Signal Process 55(10):4811–4820

Wu Y, Deng L, Li G, Zhu J, Shi L (2018) Spatio-temporal backpropagation for training high-performance spiking neural networks. Front Neurosci 12:331PubMedPubMedCentral

Yang AC, Tsai SJ (2013) Is mental illness complex? From behavior to brain. Prog Neuropsychopharmacol Biol Psychiatry 45:253–257PubMed

Zhang J, Cheng W, Liu Z, Zhang K, Lei X, Yao Y, Becker B, Liu Y, Kendrick KM, Lu G et al (2016) Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders. Brain 139(8):2307–2321PubMed

Title: Deterministic characteristics of spontaneous activity detected by multi-fractal analysis in a spiking neural network with long-tailed distributions of synaptic weights
Authors: Sou Nobukawa
Nobuhiko Wagatsuma
Haruhiko Nishimura
Publication date: 24-06-2020
Publisher: Springer Netherlands
Published in: Cognitive Neurodynamics / Issue 6/2020
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI: https://doi.org/10.1007/s11571-020-09605-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 6/2020

Characterizing the brain’s dynamical response from scalp-level neural electrical signals: a review of methodology development

The thermodynamic brain and the evolution of intellect: the role of mental energy

Fast fronto-parietal cortical dynamics of conflict detection and context updating in a flanker task

Brain connectivity analysis in fathers of children with autism

Probabilistically segregated neural circuits and subcritical linguistics

Functional-pathway-dominant contrast adaptation and sensitization in mouse retinal ganglion cells