Article
Engineering, Mechanical
Marius E. E. Yamakou, Estelle M. M. Inack
Summary: Specific network topology and STDP parameter intervals have been found to simultaneously enhance coherence resonance (CR) and stochastic synchronization (SS) in neural networks. These results suggest that optimizing the background noise, STDP rule, and network topology can enhance both the time precision of firing and the synchronization in neural systems.
NONLINEAR DYNAMICS
(2023)
Article
Physics, Fluids & Plasmas
Marius E. Yamakou, Christian Kuehn
Summary: This paper investigates coherence resonance (CR) in small-world and random adaptive networks of Hodgkin-Huxley neurons driven by spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). The results show that the degree of CR strongly depends on the adjusting rate parameter P, characteristic rewiring frequency parameter F, and network topology parameters. The study suggests that STDP and HSP can jointly enhance the time precision of firing necessary for optimal information processing and transfer in neural systems.
Article
Physics, Applied
Zhiqiu Ye, Yumei Yang, Ya Jia
Summary: This paper investigates the inhibitory effect of noise on neural discharge activity and its mechanism, and discovers the phenomenon of inverse stochastic resonance. The findings are of great importance for understanding complex physiological phenomena of the nervous systems.
INTERNATIONAL JOURNAL OF MODERN PHYSICS B
(2023)
Article
Physics, Multidisciplinary
Lu Peng, Jun Tang, Jun Ma, Jinming Luo
Summary: The synchronization of the nervous system is closely related to diseases. This study investigates the influence of autapse on neural network synchronization and shows that increasing coupling intensity can disrupt the synchronization of neural firing pattern. The transmission time delay is a key factor that complicates the synchronization process for different types of autapses.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Physics, Multidisciplinary
Guofang Li, Xiaojuan Sun
Summary: This study focuses on the effects of hybrid chemical and electrical synapses, noise, and time delay on the coherence resonance of a neuronal network. The results show that coherence resonance is better when the ratio of chemical synapses to electrical synapses approaches odd ratios. It is also observed that introducing time delay generates multiple coherence resonances, independent of the ratio of synapses. Additionally, increasing the number of subnetworks weakens or eliminates the resonance behavior.
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Lianghui Qu, Lin Du, Zilu Cao, Haiwei Hu, Zichen Deng
Summary: The study found that the random distribution of chemical autapses has a significant regulatory effect on the electrophysiological activities of neuronal networks, promoting the transmission of neural signals, inducing network-level stochastic resonance, and leading to a transition in network dynamics. Additionally, the random distribution of autapses can also induce phase synchronization phenomena in subthreshold or chaotic neuronal networks, eventually evolving into synchronous periodic discharge state.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Physics, Multidisciplinary
Daniel De la Cruz, Rodrigo Mendez-Ramirez, Adrian Arellano-Delgado, Cesar Cruz-Hernandez
Summary: This paper presents the synchronization and encrypted communication transmissions of analog and digital messages in a deterministic small-world network (DSWN). The study starts with a network of 3 coupled nodes in a nearest-neighbor (NN) topology and gradually increases the number of nodes to achieve a DSWN with 24 nodes. The experimental presentation of synchronization and encrypted communication transmissions in a DSWN is conducted using Chua's chaotic circuit as a node, with operational amplifiers (OA) for the continuous version (CV) and Euler's numerical algorithm implemented in an embedded system utilizing an Altera/Intel FPGA and external digital-to-analog converters for the discretized version (DV).
Article
Computer Science, Interdisciplinary Applications
Allan G. S. Sanchez, C. Posadas-Castillo, E. Garza-Gonzalez
Summary: This paper compares three small-world algorithms using fixed-size complex networks to investigate the efficiency of promoting synchronization through the small-world property. Cooperative behavior is determined by the transition point to the synchronized state, with the efficiency of the algorithms characterized by the presence of different types of synchronization. Non-linear systems exhibiting chaos are used to construct the complex networks.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2021)
Article
Physics, Multidisciplinary
Xueqin Wang, Dong Yu, Tianyu Li, Ya Jia
Summary: This paper investigates the phenomenon of logical stochastic resonance in Hodgkin-Huxley neurons under electromagnetic effects. It is found that appropriately reducing the magnitude of the external bias current allows Hodgkin-Huxley neurons to perform reliable logic operations while consuming less energy. Additionally, an appropriate increase in coupling strength in neuronal networks has a favorable impact on system logic operations. Small-world networks outperform scale-free networks for logical operations.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Adriane S. Reis, Eduardo L. Brugnago, Ricardo L. Viana, Antonio M. Batista, Kelly C. Iarosz, Ibere L. Caldas
Summary: In this study, we investigated the suppression of neuronal synchronization in a complex network using a two-dimensional discrete model coupled in a clustered network structure. We constructed a network based on a weighted human connectivity matrix and an adjacency matrix with small-world properties. Neuronal coupling was achieved through a chemical synapse term and a neuronal activation function. By applying a mathematical tool based on deep brain stimulation, we successfully suppressed burst phase synchronization in the network. Our results were effective in both the global network and cortical regions, demonstrating the possibility of efficiently controlling isolated cortical areas for synchronization suppression.
Article
Physics, Multidisciplinary
Tayebe Nikfard, Yahya Hematyar Tabatabaei, Reihaneh Kouhi Esfahani, Farhad Shahbazi
Summary: This study examines the synchronization of a group of identical phase oscillators in small-world networks driven by external random forces. The partially phase-synchronized states in the network become more synchronized as the gamma value increases to an optimum level before decreasing. Stochastic synchronization in the system is attributed to the weakening and destruction of topological defects in the phase-synchronized attractors of the identical oscillators in the Kuramoto model.
EUROPEAN PHYSICAL JOURNAL PLUS
(2021)
Article
Engineering, Mechanical
Guowei Wang, Lijian Yang, Xuan Zhan, Anbang Li, Ya Jia
Summary: This study investigates the influence of electromagnetic induction on chaotic resonance phenomenon in neuronal network motifs. The results show that electromagnetic induction can enhance the detection ability of neurons for weak signals, and there exists an optimal chaotic current intensity for achieving the best weak signal detection. Additionally, adjusting the parameters of electromagnetic induction can lead to more pronounced chaotic resonance phenomenon in certain network motifs compared to others.
NONLINEAR DYNAMICS
(2022)
Article
Mathematics, Applied
Sara Ameli, Maryam Karimian, Farhad Shahbazi
Summary: The study revealed synchronization enhancement in small-world networks of identical coupled phase oscillators through Kuramoto interaction and uniform time delay, with discontinuous transitions observed from partially synchronized state to a glassy phase. Bimodal frequency distributions and hysteresis loops were identified as indicators of the discontinuous nature of these transitions, alongside the existence of Chimera states at the onset of transitions.
Article
Neurosciences
Quan Xu, Tong Liu, Shoukui Ding, Han Bao, Ze Li, Bei Chen
Summary: This paper investigates the memristive electromagnetic induction effect in a bi-neuron network with heterogeneous neurons. Theoretical analysis reveals the stability of the network depends on memristor coupling strength and initial conditions. Numerical simulations demonstrate various dynamic behaviors and phase synchronization. Hardware experiments are conducted to confirm the results.
COGNITIVE NEURODYNAMICS
(2023)
Article
Computer Science, Information Systems
Zhenlong Xiao, Xin Wang, Ji Huang, Lin Hong
Summary: Knowledge synchronization in robot swarm systems is a challenging task under communication constraints. This study proposes a novel dynamic small-world network model to address this issue and demonstrates its effectiveness through simulation.