Synchronization and inter-layer interactions of noise-driven neural networks

Anis Yuniati, Te Lun Mai, Chi Ming Chen*


研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)


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 STDPrules. We described how these networks transited from a non-synchronous 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, grid, and lognormal distributions) and two types of inter-layer connections (randomand preferential connections). Among these scenarios, we concluded that the repair mechanismhas the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

期刊Frontiers in Computational Neuroscience
出版狀態已發佈 - 2017 1月 31

ASJC Scopus subject areas

  • 神經科學(雜項)
  • 細胞與分子神經科學


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