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Nervous system information processing depends on a basic neural mechanism—phase synchronization. Previous studies have shown that the precise phase with which individual neurons are synchronized to some band might have consequences on further processing and for spike timing-dependent plasticity. Based on the theory of phase synchronization, we provide new insights into scientific findings, which may help to study the neurobiological phenomenon, so as to provide support for medical diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, synchronization in two coupled neurons with spiking, bursting and chaos firings is investigated as the coupling strength gets increased. Synchronization state can be identified by means of the bifurcation diagram, the correlation coefficient and ISI-distance. It is illustrated that the coupled neurons can exhibit different types of synchronization state when the coupling strength increases. The different synchronization processes appear similar, but their detailed processes are different depending on the parameter values. The synchronization of neuronal network with two different network connectivity patterns is also studied. It is shown that chaotic and high period pattern are more difficult to get complete synchronization than the situation in single spike and low period pattern. It is also demonstrated that the synchronization status of multiple neurons is dependent on the network connectivity patterns. These results may be instructive to understand synchronization in neuronal systems. [ABSTRACT FROM AUTHOR]

Abstract: An adaptive learning rule of synapses was proposed for a general asymmetric neural network. Its feasibility was proved by the Lasalle principle. Numerical simulation results show that synaptic connection weight can converge to an appropriate strength and the network comes to synchronization. Furthermore, ISI (inter-spike interval) of synchronization orbit in neural network has a typical period doubling bifurcation. It is a further improvement compared with bifurcation of the traditional single neuron model, which promotes our understanding of neuron population activities. [Copyright &y& Elsevier]

In order to explore the dynamic characteristics of neural coding in the transmission of neural information in the brain, a model of neural network consisting of three neuronal populations is proposed in this paper using the theory of stochastic phase dynamics. Based on the model established, the neural phase synchronization motion and neural coding under spontaneous activity and stimulation are examined, for the case of varying network structure. Our analysis shows that, under the condition of spontaneous activity, the characteristics of phase neural coding are unrelated to the number of neurons participated in neural firing within the neuronal populations. The result of numerical simulation supports the existence of sparse coding within the brain, and verifies the crucial importance of the magnitudes of the coupling coefficients in neural information processing as well as the completely different information processing capability of neural information transmission in both serial and parallel couplings. The result also testifies that under external stimulation, the bigger the number of neurons in a neuronal population, the more the stimulation influences the phase synchronization motion and neural coding evolution in other neuronal populations. We verify numerically the experimental result in neurobiology that the reduction of the coupling coefficient between neuronal populations implies the enhancement of lateral inhibition function in neural networks, with the enhancement equivalent to depressing neuronal excitability threshold. Thus, the neuronal populations tend to have a stronger reaction under the same stimulation, and more neurons get excited, leading to more neurons participating in neural coding and phase synchronization motion. [ABSTRACT FROM AUTHOR]

Abstract: Under the premise of analysis on the dynamic characteristics of the transmission mechanism among the synapses, this paper has modified the coupling term in the Tass’s stochastic evolution model of neuronal oscillator population, introduced the variable higher-order coupling term. Then, we have performed the numerical simulation on the modified model. The simulation result shows that the variable coupling mechanism can induce the transition between different cluster states of the neuronal oscillator population, without the external stimulation. Another result from the numerical simulation is that, in the transient process between two different synchronization states caused by the variable coupling mechanism, it is allowed to have a full desynchronization state for a period. However, after the period of desynchronization state, the neuronal oscillator population can still reenter a new synchronization state under the action of the coupling term with the order different from initial condition. [ABSTRACT FROM AUTHOR]

Abstract: A stochastic model of neuronal population with excitatory and inhibitory connections is proposed, where excitatory synaptic dynamics is considered. Oscillatory synchronized firing patterns of a neuronal population by means of firing density are investigated. Numerical simulations using Fokker–Planck equation show that slow inhibitory connection contributes to oscillatory synchronized firing of the neuronal population, and synchronous activity is enhanced due to inhibitory connection. The effect of time delay on the oscillatory synchronized firing in the neuronal population using phase mode is explored. Numerical simulation indicates that short synaptic transmission delay can suppress oscillatory synchronized firing, but this suppression is instable. [Copyright &y& Elsevier]

In this letter, taking into account the variation of coupling strength, we present a dynamical model of neuronal oscillator population subject to external stimulus and noise, and introduce an average number density of coupling pairs to describe the synchronization behavior of the neuronal oscillator population. The numerical simulations indicate that external stimulus enhances the synchronized activity in neuronal population and the coupling between the neuronal oscillators. With a stronger stimulus, the synchronization in neuronal population emerges rapidly, and the duration of the synchronization is longer. The numerical simulations also show that when the combined stimuli of different modalities are performed simultaneously, there is a reciprocal or competitive interaction between the different stimulus modalities. [ABSTRACT FROM AUTHOR]