Synchronization and Rhythm Transition in a Complex Neuronal Network

Abstract

Synchronization and rhythm transition in an excitatory-inhibitory balanced cortical neuronal network are investigated in this paper. A small-world neuronal network is performed to be the cortical region of cerebral cortex, which is composed of different types of Izhikevich neurons. The combination of regular spiking (RS) cells, chattering (CH) cells, or mixed RS and CH cells are imitated as excitatory neurons, whereas fast spiking (FS) cells mimic inhibitory neurons. We mainly focus on the effect of different types of neurons on synchronization and rhythm transition of the neuronal network. The simulation results illustrate that it is easier for the neuronal network with CH excitatory neurons to achieve synchronization, and it is also more susceptible to the parameters than the network with RS neurons. Together with the weight of the synaptic connection, the structure of the small-world network is also explored to identify its influence on the synchronization and the rhythm transition. Moreover, we found that the synchronization performance could be improved by increasing the degree of influence among neurons. Importantly, the synchronization states are associated with the rhythm transitions. Especially, the consistency of synchronization of the neuronal network and γ band rhythm is illustrated. In addition, our results could have an important significance on further understanding brain rhythms and synchrony in neuroscience.

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

“…The neuron cluster is in the inhomogeneously asynchronous state, and the redundancy between neurons is lower, and the network interaction ability is weaker than the network computing ability. When w e = 0.35, all of the neurons are in the complete synchronous firing state ( Figure 10C ) (Wang et al, 2020 ). When the coupling strength reaches a specific strength ( w e = 1), the MIZH spiking network exhibits another spatiotemporal pattern (the top plot in Figure 10D ).…”

“…The synchronous neural phenomenon is involved in a series of higher-level brain functions and neuronal processes (Liu and Shi, 2018 ). Synchronous activities play a crucial role in information processing and signal encoding of the cerebral cortex (Wang et al, 2020 ). The asynchronous behavior is the pivotal part of the intermediary between information propagation and transformation.…”

The Synchronization Behaviors of Memristive Synapse-Coupled Fractional-Order Neuronal Networks

“…Content may change prior to final publication. networks [4][5][6][7][8][9][10][11][12][13][14][15]. The relevant literature shows that the main factors affecting the synchronization of coupled neuronal networks are the network topology [4][5][6][7][8], time delay in the network [9,10], coupling strength [11][12][13][14], and multilayer network structure [15], etc.…”

“…networks [4][5][6][7][8][9][10][11][12][13][14][15]. The relevant literature shows that the main factors affecting the synchronization of coupled neuronal networks are the network topology [4][5][6][7][8], time delay in the network [9,10], coupling strength [11][12][13][14], and multilayer network structure [15], etc. Recently, researchers have found that the electrical activity of neurons may be affected by electromagnetic fields, and thus, electromagnetic fields can be detected by the neuronal system [16,17].…”

Data-Driven Optimal Synchronization for Complex Networks With Unknown Dynamics

“…R ECENTLY, the complex networks (CNs) have gained growing attentions because of their widely applications such as smart grids, social networks, communication networks and so on [1]- [5]. One of the emphases for CNs research is the synchronization problem, which refers to a coherent phenomenon between two or more systems.…”