Article
Mathematics, Interdisciplinary Applications
A. Moujahid, F. Vadillo
Summary: Mathematical modeling is crucial for studying the impact of delay in neural systems and evaluating its effects on the signaling activity of coupled neurons. This study focuses on the energy perspective of delayed coupling in Hindmarsh-Rose burst neurons, examining the average energy consumption required to maintain cooperative behavior and quantifying the contribution of synapses to total energy consumption.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Mathematical & Computational Biology
Zhen Wang, Ramesh Ramamoorthy, Xiaojian Xi, Hamidreza Namazi
Summary: This study presents a mathematical modeling study on the synchronization among neurons connected by transient electrical synapses transformed to chemical synapses over time. The results show that the transient synapses lead to burst synchronization of the neurons while the neurons are resting when both synapses exist constantly. The period of the transitions and the time of presence of electrical synapses to chemical ones are effective on the synchronization.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yuto Yoshikai, Tianyi Zheng, Kiyoshi Kotani, Yasuhiko Jimbo
Summary: In this study, a bursting neuron model was proposed to analyze the bursting ratio and phase response function. The neuronal population dynamics composed of bursting excitatory neurons mixed with inhibitory neurons were theoretically analyzed. Bifurcation analysis revealed three types of gamma oscillations under different interaction strengths, and the adjoint method of the Fokker-Planck equation showed that inhibitory doublet facilitates synchronization of high-frequency oscillations. Decreasing the bursting ratio of individual neurons increased the relative high-gamma component of the populational phase-coupling functions and improved the ability of the neuronal population model to synchronize with faster oscillatory input. This study provides insight into the dynamics of bursting neuron populations and emphasizes their role in rhythmic activities.
NEURAL COMPUTATION
(2023)
Article
Mathematics, Applied
Pau Clusella, Bastian Pietras, Ernest Montbrio
Summary: Researchers derive a Kuramoto model corresponding to a weakly coupled population of nearly identical quadratic integrate-and-fire neurons with both electrical and chemical coupling. They find that the ratio of chemical to electrical coupling critically determines the synchronization properties of the network. They also demonstrate that the presence of both electrical and chemical coupling is necessary for chimera states to exist.
Article
Mathematics, Applied
Luciano A. Magrini, Margarete Oliveira Domingues, Elbert E. N. Macau, Istvan Z. Kiss
Summary: This study investigated synchronization of coupled electrochemical bursting oscillators through the electrodissolution of iron in sulfuric acid. The results showed a progressive transition with increasing coupling strength, where synchronization between oscillators went from overlapping fast burst intervals, to synchronization of fast spiking, and finally to synchronization of slow chaotic oscillations. In a population of 25 globally coupled oscillators, coupling eliminated fast dynamics, leaving only synchronization of slow dynamics.
Article
Mathematics, Applied
XinYue Chen, Ran Chen, YiLin Sun, Shuai Liu
Summary: In this study, we examined the effect of coupling scheme asymmetry on oscillator dynamics in a star network. Through numerical and analytical approaches, we determined stability conditions for various collective behavior states, ranging from equilibrium point to complete synchronization and quenched hub incoherence to remote synchronization. The coupling asymmetry factor alpha was found to significantly influence and determine the stable parameter region of each state. The findings, supported by theoretical analysis and validated through numerical simulations, offer practical methods for controlling, restoring, or obstructing specific collective behavior.
Article
Engineering, Electrical & Electronic
Hairong Lin, Chunhua Wang, Chengjie Chen, Yichuang Sun, Chao Zhou, Cong Xu, Qinghui Hong
Summary: This paper investigates neural bursting and synchronization by modeling two neural network models based on the Hopfield neural network, showing that these networks can generate rich dynamic behaviors. The synchronization dynamics of the coupling neural network can produce different types of synchronous behaviors depending on the synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Mathematics, Applied
Eugenio Urdapilleta
Summary: Inhibitory neurons form an extensive network in the cerebral cortex, playing a crucial role in the development of different rhythms. The transition from incoherent to synchronized state is important, and effective synaptic connectivity patterns may support this transition. Additionally, an adaptive mechanism has been built to rapidly generate the underlying structure of this network based on ongoing firing statistics.
Article
Neurosciences
Masoumeh Shavikloo, Asghar Esmaeili, Alireza Valizadeh, Mojtaba Madadi Asl
Summary: Synchronization is a key feature of brain dynamics and plays a crucial role in information transmission and higher brain functions. This study investigates the impact of multiple synapses on synchronization dynamics in a two-neuron system using both phase oscillator model and HH model. The results show that the symmetry/asymmetry of connections and transmission delays determine the stability of synchronization.
COGNITIVE NEURODYNAMICS
(2023)
Article
Physics, Fluids & Plasmas
Jyoti Sharma, Ishant Tiwari, P. Parmananda, M. Rivera
Summary: This study reports experiments on an active camphor rotor. It shows that an irregular rotor can be transformed into a periodic rotor with the assistance of suitable external periodic forcing. Additionally, synchronized bursting between two coupled irregular rotors is observed.
Article
Computer Science, Artificial Intelligence
Qiang Jia, Eric S. Mwanandiye, Wallace K. S. Tang
Summary: This paper investigates the master-slave synchronization problem of delayed neural networks with general time-varying control. The main theorem is established in terms of the time average of the control gain by using the Lyapunov-Razumikhin theorem, and some useful corollaries are deduced. The theorem also provides a solution for regaining stability under control failure, which is further demonstrated with numerical examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Endocrinology & Metabolism
Mehran Fazli, Richard Bertram
Summary: The endocrine cells of the anterior pituitary gland exhibit electrical activity, with bursting being the most effective pattern for hormone release. Activity synchronization is influenced by electrical coupling and physiological changes. Weak electrical coupling makes it difficult to predict the functional network.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Materials Science, Multidisciplinary
Juan Bisquert
Summary: This paper investigates the synchronization of neurons and its significance in understanding brain function and developing neural computation. By analyzing FHN neurons, the study discovers new elements and oscillatory impedance introduced by delayed interaction, which has potential applications in determining the extent of coupling between neurons experimentally.
PHYSICA STATUS SOLIDI A-APPLICATIONS AND MATERIALS SCIENCE
(2022)
Article
Mathematics, Interdisciplinary Applications
Qianqian Ren, Xiyu Liu
Summary: The article introduces a new variant of spiking neural P systems called delayed spiking neural P systems (DSN P systems), which achieve time control and system delay by setting schedules on spiking rules and forgetting rules. Additionally, the universality of DSN P systems in both generating and accepting modes is proved, along with a universal DSN P system with 81 neurons for computing functions.
Article
Mathematics, Interdisciplinary Applications
Adriane S. Reis, Kelly C. Iarosz, Fabiano A. S. Ferrari, Ibere L. Caldas, Antonio M. Batista, Ricardo L. Viana
Summary: The study explores bursting synchronization in a neuronal network model inspired by the human cerebral cortex, which can be partially or completely suppressed using an external signal based on time delay and signal intensity. The findings suggest that bursting synchronization in the network model can be related to pathological rhythms and targeted suppression techniques may have potential applications for mitigating such pathologies.
CHAOS SOLITONS & FRACTALS
(2021)
