The Experts below are selected from a list of 65019 Experts worldwide ranked by ideXlab platform
Jianfeng Feng - One of the best experts on this subject based on the ideXlab platform.
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Neuronal synfire chain via moment Neuronal Network approach
International Conference on Neural Information Processing, 2013Co-Authors: Jianfeng FengAbstract:In this letter, we use a novel method to analyse the stability synchronisation propagation in Neuronal Networks via the moment Neuronal Network approach developed recently. Here, the stability of synfire chain is twofold, including the stability of both the synchronisation in the cluster and the asynchronisation out of the cluster. Under the framework of the moment Neuronal Network, we model the dynamics of the Pearson correlation coefficients via evolution field equations. Thus, we study the stability of synfire chain via looking into the attractors of the model. Based on analytic and numerical approaches, In particular, we point out that the variance of the Neuronal spike rate should be updated with the synfire propagation. Also, we find out that the balance between the excitation and inhibition PSPs and a suitable size of the cluster can enhance the stability of synfire chain.
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ICONIP (1) - Neuronal synfire chain via moment Neuronal Network approach
Neural Information Processing, 2013Co-Authors: Jianfeng FengAbstract:In this letter, we use a novel method to analyse the stability synchronisation propagation in Neuronal Networks via the moment Neuronal Network approach developed recently. Here, the stability of synfire chain is twofold, including the stability of both the synchronisation in the cluster and the asynchronisation out of the cluster. Under the framework of the moment Neuronal Network, we model the dynamics of the Pearson correlation coefficients via evolution field equations. Thus, we study the stability of synfire chain via looking into the attractors of the model. Based on analytic and numerical approaches, In particular, we point out that the variance of the Neuronal spike rate should be updated with the synfire propagation. Also, we find out that the balance between the excitation and inhibition PSPs and a suitable size of the cluster can enhance the stability of synfire chain.
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Bifurcations of emergent bursting in a Neuronal Network
PloS one, 2012Co-Authors: Wei Lin, Gareth Leng, Jianfeng FengAbstract:Complex Neuronal Networks are an important tool to help explain paradoxical phenomena observed in biological recordings. Here we present a general approach to mathematically tackle a complex Neuronal Network so that we can fully understand the underlying mechanisms. Using a previously developed Network model of the milk-ejection reflex in oxytocin cells, we show how we can reduce a complex model with many variables and complex Network topologies to a tractable model with two variables, while retaining all key qualitative features of the original model. The approach enables us to uncover how emergent synchronous bursting can arise from a Neuronal Network which embodies known biological features. Surprisingly, the bursting mechanisms are similar to those found in other systems reported in the literature, and illustrate a generic way to exhibit emergent and multiple time scale oscillations at the membrane potential level and the firing rate level.
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Emergent synchronous bursting of oxytocin Neuronal Network
PLoS Computational Biology, 2008Co-Authors: Enrico Rossoni, Gareth Leng, Jianfeng Feng, Brunello Tirozzi, David Brown, Françoise MoosAbstract:When young suckle, they are rewarded intermittently with a let-down of milk that results from reflex secretion of the hormone oxytocin; without oxytocin, newly born young will die unless they are fostered. Oxytocin is made by magnocellular hypothalamic neurons, and is secreted from their nerve endings in the pituitary in response to action potentials (spikes) that are generated in the cell bodies and which are propagated down their axons to the nerve endings. Normally, oxytocin cells discharge asynchronously at 1–3 spikes/s, but during suckling, every 5 min or so, each discharges a brief, intense burst of spikes that release a pulse of oxytocin into the circulation. This reflex was the first, and is perhaps the best, example of a physiological role for peptide-mediated communication within the brain: it is coordinated by the release of oxytocin from the dendrites of oxytocin cells; it can be facilitated by injection of tiny amounts of oxytocin into the hypothalamus, and it can be blocked by injection of tiny amounts of oxytocin antagonist. Here we show how synchronized bursting can arise in a Neuronal Network model that incorporates basic observations of the physiology of oxytocin cells. In our model, bursting is an emergent behaviour of a complex system, involving both positive and negative feedbacks, between many sparsely connected cells. The oxytocin cells are regulated by independent afferent inputs, but they interact by local release of oxytocin and endocannabinoids. Oxytocin released from the dendrites of these cells has a positive-feedback effect, while endocannabinoids have an inhibitory effect by suppressing the afferent input to the cells.
Jun Tang - One of the best experts on this subject based on the ideXlab platform.
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a review for dynamics in neuron and Neuronal Network
Nonlinear Dynamics, 2017Co-Authors: Jun TangAbstract:The biological Hodgkin–Huxley model and its simplified versions have confirmed its effectiveness for recognizing and understanding the electrical activities in neurons, and bifurcation analysis is often used to detect the mode transition in Neuronal activities. Within the collective behaviors of neurons, Neuronal Network with different topology is designed to study the synchronization behavior and spatial pattern formation. In this review, the authors give careful comments for the presented neuron models and present some open problems in this field, nonlinear analysis could be effective to further discuss these problems and some results could be helpful to give possible guidance in the field of neurodynamics.
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delay and diversity induced synchronization transitions in a small world Neuronal Network
Physical Review E, 2011Co-Authors: Jun Tang, Hui Xia, Xianqing YangAbstract:The synchronized behaviors of a noisy small-world Neuronal Network with delay and diversity is numerically studied by calculating a synchronization measure and plotting firing pattern. We show that delay in the information transmission can induce fruitful synchronization transitions, including transition from phase locking to antiphase synchronization, and transition from antiphase synchronization to complete synchronization. Furthermore, the delay-induced complete synchronization can be changed by diversity, which causes the oscillatory-like transition between antiphase synchronization and complete synchronization.
Qunxian Zheng - One of the best experts on this subject based on the ideXlab platform.
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Synchronization of the small-world Neuronal Network with unreliable synapses.
Physical biology, 2010Co-Authors: Qunxian ZhengAbstract:As is well known, synchronization phenomena are ubiquitous in Neuronal systems. Recently a lot of work concerning the synchronization of the Neuronal Network has been accomplished. In these works, the synapses are usually considered reliable, but experimental results show that, in biological Neuronal Networks, synapses are usually unreliable. In our previous work, we have studied the synchronization of the Neuronal Network with unreliable synapses; however, we have not paid attention to the effect of topology on the synchronization of the Neuronal Network. Several recent studies have found that biological Neuronal Networks have typical properties of small-world Networks, characterized by a short path length and high clustering coefficient. In this work, mainly based on the small-world Neuronal Network (SWNN) with inhibitory neurons, we study the effect of Network topology on the synchronization of the Neuronal Network with unreliable synapses. Together with the Network topology, the effects of the GABAergic reversal potential, time delay and noise are also considered. Interestingly, we found a counter-intuitive phenomenon for the SWNN with specific shortcut adding probability, that is, the less reliable the synapses, the better the synchronization performance of the SWNN. We also consider the effects of both local noise and global noise in this work. It is shown that these two different types of noise have distinct effects on the synchronization: one is negative and the other is positive.
Xianqing Yang - One of the best experts on this subject based on the ideXlab platform.
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delay and diversity induced synchronization transitions in a small world Neuronal Network
Physical Review E, 2011Co-Authors: Jun Tang, Hui Xia, Xianqing YangAbstract:The synchronized behaviors of a noisy small-world Neuronal Network with delay and diversity is numerically studied by calculating a synchronization measure and plotting firing pattern. We show that delay in the information transmission can induce fruitful synchronization transitions, including transition from phase locking to antiphase synchronization, and transition from antiphase synchronization to complete synchronization. Furthermore, the delay-induced complete synchronization can be changed by diversity, which causes the oscillatory-like transition between antiphase synchronization and complete synchronization.
An Wang - One of the best experts on this subject based on the ideXlab platform.
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Synchronous firing patterns and transitions in small-world Neuronal Network
Nonlinear Dynamics, 2015Co-Authors: Guanping Wang, Wuyin Jin, An WangAbstract:Synchronous firing patterns and transitions of Hindmarsh–Rose small-world Neuronal Network are studied in this work. With the same external stimuli current, it is shown that the synchronous state of the Newman and Watts (NW) small-world Neuronal Network is completely determined by its external stimuli current, have nothing about the coupling strength \(C\) as well as the connection probability \(p\); a qualitative explanation is proposed for the opposite changes of the minimum critical coupling strength \(C_{\mathrm{mcc}}\) and the minimum connection probability \(p_{\mathrm{mcp}}\). With two different amplitude stimuli current, only almost complete synchronization can be achieved, and the firing patterns of the synchronized NW small-world Neuronal Network only distributes the interval with two endpoints, which just correspond to the two modes in a single model neuron with these two kinds of amplitude stimuli current.
