title: Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.
abstract: 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 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 (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.
authors.affiliation: Department of Physics, National Taiwan Normal University Taipei, Taiwan.
authors.forename: Anis
authors.initials: A
authors.lastname: Yuniati
authors.affiliation: Department of Physics, National Taiwan Normal University Taipei, Taiwan.
authors.forename: Te-Lun
authors.initials: TL
authors.lastname: Mai
authors.affiliation: Department of Physics, National Taiwan Normal University Taipei, Taiwan.
authors.forename: Chi-Ming
authors.initials: CM
authors.lastname: Chen
keywords: biological neural networks; computer simulation; developing neural networks; inter-layer interactions; noise-driven synchronization; repair mechanism of neural networks; spike-timing-dependent plasticity; synchronous firing