title: Long-period rhythmic synchronous firing in a scale-free network.
abstract: Stimulus information is encoded in the spatial-temporal structures of external inputs to the neural system. The ability to extract the temporal information of inputs is fundamental to brain function. It has been found that the neural system can memorize temporal intervals of visual inputs in the order of seconds. Here we investigate whether the intrinsic dynamics of a large-size neural circuit alone can achieve this goal. The network models we consider have scale-free topology and the property that hub neurons are difficult to be activated. The latter is implemented by either including abundant electrical synapses between neurons or considering chemical synapses whose efficacy decreases with the connectivity of the postsynaptic neuron. We find that hub neurons trigger synchronous firing across the network, loops formed by low-degree neurons determine the rhythm of synchronous firing, and the hardness of exciting hub neurons avoids epileptic firing of the network. Our model successfully reproduces the experimentally observed rhythmic synchronous firing with long periods and supports the notion that the neural system can process temporal information through the dynamics of local circuits in a distributed way.
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and International Digital Group (IDG)/McGovern Institute for Brain Research, Department of Physics, and Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Mi
authors.forename: Xuhong
authors.initials: X
authors.lastname: Liao
authors.forename: Xuhui
authors.initials: X
authors.lastname: Huang
authors.forename: Lisheng
authors.initials: L
authors.lastname: Zhang
authors.forename: Weifeng
authors.initials: W
authors.lastname: Gu
authors.forename: Gang
authors.initials: G
authors.lastname: Hu
authors.forename: Si
authors.initials: S
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000378:metabolism*; D010507:Periodicity; D013569:Synapses / Q000378:metabolism*; D013997:Time Factors; D014796:Visual Perception / Q000502:physiology
keywords: reservoir network; scaled chemical synapse; temporal information processing