We study the evolutionary snowdrift game in a heterogeneous Newman-Watts small-world network. The heterogeneity of the network is controlled by the number of hubs. It is found that the moderate heterogeneity of the ne...We study the evolutionary snowdrift game in a heterogeneous Newman-Watts small-world network. The heterogeneity of the network is controlled by the number of hubs. It is found that the moderate heterogeneity of the network can promote the cooperation best. Besides, we study how the hubs affect the evolution of cooperative behaviours of the heterogeneous Newman-Watts small-world network. Simulation results show that both the initial states of hubs and the connections between hubs can play an important role. Our work gives a further insight into the effect of hubs on the heterogeneous networks.展开更多
In this paper, we study how adaptive coupling with time-periodic growth speed (TPGS) affects the spiking synchronization of weighted adaptive Newman-Watts Hodgkin-Huxley neuron networks with time delays. It is found t...In this paper, we study how adaptive coupling with time-periodic growth speed (TPGS) affects the spiking synchronization of weighted adaptive Newman-Watts Hodgkin-Huxley neuron networks with time delays. It is found that the neuronal spiking intermittently exhibits synchronization transitions between desynchronization and in-phase synchronization or anti-phase synchronization as TPGS amplitude or frequency is varied, showing multiple synchronization transitions. These transitions depend on the values of time delay and can occur only when time delay is close to those values that can induce synchronization transitions when the growth speed is fixed. These results show that the adaptive coupling with TPGS has great influence on the spiking synchronization of the neuronal networks and thus plays a crucial role in the information processing and transmission in neural systems.展开更多
In this paper,we study how information transmission delays affect the spiking behavior of electrically coupled stochastic Hodgkin-Huxley (HH) neurons on Newman-Watts networks.It is found that the spiking behavior beco...In this paper,we study how information transmission delays affect the spiking behavior of electrically coupled stochastic Hodgkin-Huxley (HH) neurons on Newman-Watts networks.It is found that the spiking behavior becomes the most regular at an optimal time delay,indicating the occurrence of delay-induced coherence resonance-like (CR-like) behavior.Interestingly,there are different CR-like types,depending on the membrane patch size of the neuron.For a smaller patch size,only single CR-like behavior occurs;while for a larger patch size,coherence bi-resonance-like (CBR) behavior appears.These findings show that the delay-induced CR-like behavior is closely related to the channel noise strength,and the coupled neurons may exhibit different spiking behaviors under the interplay of the channel noise and time delay.Therefore,the channel noise should be taken into account in the study of time delay-related spiking activity in stochastic HH neurons.This work provides new insight into the role of channel noise and information transmission delays in realistic neural systems.展开更多
基金supported by the National Basic Research Program of China (No 2006CB705500)the National Natural Science Foundation of China (Grant Nos 60744003, 10635040, 10532060 and 10472116)the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘We study the evolutionary snowdrift game in a heterogeneous Newman-Watts small-world network. The heterogeneity of the network is controlled by the number of hubs. It is found that the moderate heterogeneity of the network can promote the cooperation best. Besides, we study how the hubs affect the evolution of cooperative behaviours of the heterogeneous Newman-Watts small-world network. Simulation results show that both the initial states of hubs and the connections between hubs can play an important role. Our work gives a further insight into the effect of hubs on the heterogeneous networks.
基金financially supported by the Natural Science Foundation of Shandong Province of China (ZR2012AM013)
文摘In this paper, we study how adaptive coupling with time-periodic growth speed (TPGS) affects the spiking synchronization of weighted adaptive Newman-Watts Hodgkin-Huxley neuron networks with time delays. It is found that the neuronal spiking intermittently exhibits synchronization transitions between desynchronization and in-phase synchronization or anti-phase synchronization as TPGS amplitude or frequency is varied, showing multiple synchronization transitions. These transitions depend on the values of time delay and can occur only when time delay is close to those values that can induce synchronization transitions when the growth speed is fixed. These results show that the adaptive coupling with TPGS has great influence on the spiking synchronization of the neuronal networks and thus plays a crucial role in the information processing and transmission in neural systems.
基金supported by the Natural Science Foundation of Shandong Province in China (ZR2009AM016)
文摘In this paper,we study how information transmission delays affect the spiking behavior of electrically coupled stochastic Hodgkin-Huxley (HH) neurons on Newman-Watts networks.It is found that the spiking behavior becomes the most regular at an optimal time delay,indicating the occurrence of delay-induced coherence resonance-like (CR-like) behavior.Interestingly,there are different CR-like types,depending on the membrane patch size of the neuron.For a smaller patch size,only single CR-like behavior occurs;while for a larger patch size,coherence bi-resonance-like (CBR) behavior appears.These findings show that the delay-induced CR-like behavior is closely related to the channel noise strength,and the coupled neurons may exhibit different spiking behaviors under the interplay of the channel noise and time delay.Therefore,the channel noise should be taken into account in the study of time delay-related spiking activity in stochastic HH neurons.This work provides new insight into the role of channel noise and information transmission delays in realistic neural systems.
