Inter-layer interactions of noise-driven neural network

dc.contributorChen, Chi-Mingzh_TW
dc.contributorChen, Chi-Mingen_US
dc.contributor.authorYou Annizh_TW
dc.contributor.authorAnis Yuniatien_US
dc.date.accessioned2019-09-05T02:11:19Z
dc.date.available不公開
dc.date.available2019-09-05T02:11:19Z
dc.date.issued2017
dc.description.abstractNonezh_TW
dc.description.abstractThe role of neurons in the brain as an information processing system has attracted considerable attentions and motivated the development of various research techniques. Computationally simulating the interaction among neurons is very useful in investigating the complexity of a neural network. Developed neural networks may exhibit complex dynamic behaviors, such as synchronization and clustered synchronous firing. In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a nonsynchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, uniform, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.en_US
dc.description.sponsorship物理學系zh_TW
dc.identifierG080141010S
dc.identifier.urihttp://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G080141010S%22.&%22.id.&
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/102535
dc.language英文
dc.subjectbiological neural networkszh_TW
dc.subjectinter-layer interactionszh_TW
dc.subjectnoise-driven synchronizationzh_TW
dc.subjectspike-timing-dependent plasticityzh_TW
dc.subjectsynchronous firingzh_TW
dc.subjectcomputer simulationzh_TW
dc.subjectdeveloping neural networkszh_TW
dc.subjectrepair mechanism of neural networkszh_TW
dc.subjectbiological neural networksen_US
dc.subjectinter-layer interactionsen_US
dc.subjectnoise-driven synchronizationen_US
dc.subjectspike-timing-dependent plasticityen_US
dc.subjectsynchronous firingen_US
dc.subjectcomputer simulationen_US
dc.subjectdeveloping neural networksen_US
dc.subjectrepair mechanism of neural networksen_US
dc.titleInter-layer interactions of noise-driven neural networkzh_TW
dc.titleInter-layer interactions of noise-driven neural networken_US

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