title: Rhythmic Oscillations of Excitatory Bursting Hodkin-Huxley Neuronal Network with Synaptic Learning.
abstract: Rhythmic oscillations of neuronal network are actually kind of synchronous behaviors, which play an important role in neural systems. In this paper, the properties of excitement degree and oscillation frequency of excitatory bursting Hodkin-Huxley neuronal network which incorporates a synaptic learning rule are studied. The effects of coupling strength, synaptic learning rate, and other parameters of chemical synapses, such as synaptic delay and decay time constant, are explored, respectively. It is found that the increase of the coupling strength can weaken the extent of excitement, whereas increasing the synaptic learning rate makes the network more excited in a certain range; along with the increasing of the delay time and the decay time constant, the excitement degree increases at the beginning, then decreases, and keeps stable. It is also found that, along with the increase of the synaptic learning rate, the coupling strength, the delay time, and the decay time constant, the oscillation frequency of the network decreases monotonically.
authors.affiliation: Engineering Research Center of Digitized Textile & Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Shi
authors.affiliation: Engineering Research Center of Digitized Textile & Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Han
authors.affiliation: Engineering Research Center of Digitized Textile & Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China.
authors.forename: Zhijie
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Engineering Research Center of Digitized Textile & Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China.
authors.forename: Caiyun
authors.initials: C
authors.lastname: Li
mesh_terms: D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010507:Periodicity; D013569:Synapses / Q000502:physiology*