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题名: Latching chains in K-nearest-neighbor and modular small-world networks
作者: Song SM(宋三明); Yao HX(姚鸿勋); Simonov, Alexander Yurievich
作者部门: 海洋信息技术装备中心
关键词: Associative retrieval ; latching chain ; modular structure ; Potts network ; sequential activity ; small-world
刊名: NETWORK-COMPUTATION IN NEURAL SYSTEMS
ISSN号: 0954-898X
出版日期: 2015
卷号: 26, 期号:1, 页码:1-24
收录类别: SCI
产权排序: 1
项目资助者: National Natural Science Foundation of China [61071180] ; Key Program of the National Natural Science Foundation of China [61133003] ; China Postdoctoral Science Foundation [2014M561266] ; Jiang Xinsong Innovation Fund [Y4FC012901] ; Russian President Scholarship [SP-991.2012.4] ; Russian President Grant [MK-4602.2013.4] ; Russian Ministry of education and science [02.B.49.21.0003] ; Russian Foundation for Basic Research [13-02-01223, 13-04-12041] ; Russian Science Foundation [14-11-00693]
摘要: Latching dynamics retrieve pattern sequences successively by neural adaption and pattern correlation. We have previously proposed a modular latching chain model in Song et al. (2014) to better accommodate the structured transitions in the brain. Different cortical areas have different network structures. To explore how structural parameters like rewiring probability, threshold, noise and feedback connections affect the latching dynamics, two different connection schemes, K-nearest-neighbor network and modular network both having modular structure are considered. Latching chains are measured using two proposed measures characterizing length of intra-modular latching chains and sequential inter-modular association transitions. Our main findings include: (1) With decreasing threshold coefficient and rewiring probability, both the K-nearest-neighbor network and the modular network experience quantitatively similar phase change processes. (2) The modular network exhibits selectively enhanced latching in the small-world range of connectivity. (3) The K-nearest-neighbor network is more robust to changes in rewiring probability, while the modular network is more robust to the presence of noise pattern pairs and to changes in the strength of feedback connections. According to our findings, the relationships between latching chains in K-nearest-neighbor and modular networks and different forms of cognition and information processing emerging in the brain are discussed.
语种: 英语
WOS记录号: WOS:000352479200001
WOS标题词: Science & Technology ; Technology ; Life Sciences & Biomedicine
类目[WOS]: Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Neurosciences
关键词[WOS]: NEURAL-NETWORKS ; ASSOCIATIVE MEMORY ; CORTICAL ACTIVITY ; FRONTAL-CORTEX ; BRAIN NETWORKS ; MODELS ; NEURONS ; LANGUAGE ; DYNAMICS
研究领域[WOS]: Computer Science ; Engineering ; Neurosciences & Neurology
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/16129
Appears in Collections:海洋信息技术装备中心_期刊论文

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