Synchronization in a temporal multiplex neuronal hypernetwork

Rakshit, Sarbendu; Bera, Bidesh K.; Ghosh, Dibakar

doi:10.1103/physreve.98.032305

Cited by 75 publications

(32 citation statements)

References 74 publications

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“…Recently, the synchronisation in neuronal networks in presence of both excitatory and inhibitory synapses has been observed using neuronal models (Bera et al, 2019a;Pal et al, 2021) coupled through hypernetworks (Rakshit et al, 2018a;Bera et al, 2019b) and multiplex configurations (Rakshit et al, 2018b). In cortico-cortical communication, one cortical area can interact with other one by means of excitatory and inhibitory connectivities (Roland et al, 2014;Tamioka et al, 2015;Tovete et al, 2015;D'Souza et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Emergence of Neuronal Synchronisation in Coupled Areas

Protachevicz

Hansen

Iarosz

et al. 2021

Front. Comput. Neurosci.

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One of the most fundamental questions in the field of neuroscience is the emergence of synchronous behaviour in the brain, such as phase, anti-phase, and shift-phase synchronisation. In this work, we investigate how the connectivity between brain areas can influence the phase angle and the neuronal synchronisation. To do this, we consider brain areas connected by means of excitatory and inhibitory synapses, in which the neuron dynamics is given by the adaptive exponential integrate-and-fire model. Our simulations suggest that excitatory and inhibitory connections from one area to another play a crucial role in the emergence of these types of synchronisation. Thus, in the case of unidirectional interaction, we observe that the phase angles of the neurons in the receiver area depend on the excitatory and inhibitory synapses which arrive from the sender area. Moreover, when the neurons in the sender area are synchronised, the phase angle variability of the receiver area can be reduced for some conductance values between the areas. For bidirectional interactions, we find that phase and anti-phase synchronisation can emerge due to excitatory and inhibitory connections. We also verify, for a strong inhibitory-to-excitatory interaction, the existence of silent neuronal activities, namely a large number of excitatory neurons that remain in silence for a long time.

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Section: Introductionmentioning

confidence: 99%

Emergence of Neuronal Synchronisation in Coupled Areas

Protachevicz

Hansen

Iarosz

et al. 2021

Front. Comput. Neurosci.

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“…Collective dynamics on temporal networks, in spite of their importance, were far less studied. In this regard, there was a line of work on synchronization and stability in temporal networks [10,32,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. For example, moving agent networks were studied [36,37,41,45], where interactions among the agents are switched on when they are sufficiently close in the physical space.…”

Section: Introductionmentioning

confidence: 99%

Random temporal connections promote network synchronization

et al. 2019

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In this article, we report a phenomenon of collective dynamics on discrete-time complex networks: random temporal interaction matrix even of zero or/and small average are able to significantly enhance synchronization with probability one. According to current knowledge, there is no verifiably sufficient criterion on what kind of the random temporal interaction matrix of zero or/and small average to induce any kind of coherence in the discrete-time temporal network with probability one. We use the standard method of synchronization analytics and the theory of stochastic processes to establish such a kind of criterion, by which we rigorously and accurately depict how synchronization occurring with probability one is affected by the statistical characteristics of the random temporal connections, such as the strength and topology of the connections as well as their probability distributions. We also illustrate the enhancement phenomenon using physical and biological complex dynamical networks.

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“…Among them, one pioneering explorations are synchronization properties [8,9] of coupled network and these phenomena have been mostly studied in the last few decades for its immense applicability in various physical [10] and engineered systems [11]. Previously, different types of synchrony properties have been studied in multiplex network organization such as cluster synchronization [12], interlayer synchronization [13,14], intralayer synchronization [15][16][17], the coexistence of synchrony and asynchrony [18][19][20], and explosive synchronization [21]. But in most cases, the network structures are considered as static networks which means that the underlying connections are invariant with respect to time.…”

Section: Introductionmentioning

confidence: 99%

Enhancing synchrony in multiplex network due to rewiring frequency

et al. 2019

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Most of the previous studies on synchrony in multiplex networks have been investigated using different types of intralayer network architectures which are either static or temporal. Effect of a temporal layer on intralayer synchrony in a multilayered network still remains elusive. In this paper, we discuss intralayer synchrony in a multiplex network consisting of static and temporal layers and how a temporal layer influences other static layers to enhance synchrony simultaneously. We analytically derive local stability conditions for intralayer synchrony based on the master stability function approach. The analytically derived results are illustrated by numerical simulations on up to five-layers multiplex networks with the paradigmatic Lorenz system as the node dynamics in each individual layer.

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Synchronization in a temporal multiplex neuronal hypernetwork

Cited by 75 publications

References 74 publications

Emergence of Neuronal Synchronisation in Coupled Areas

Emergence of Neuronal Synchronisation in Coupled Areas

Random temporal connections promote network synchronization

Enhancing synchrony in multiplex network due to rewiring frequency

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