@ARTICLE{10.3389/fncom.2017.00002, AUTHOR={Yuniati, Anis and Mai, Te-Lun and Chen, Chi-Ming}, TITLE={Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks}, JOURNAL={Frontiers in Computational Neuroscience}, VOLUME={11}, YEAR={2017}, URL={https://www.frontiersin.org/articles/10.3389/fncom.2017.00002}, DOI={10.3389/fncom.2017.00002}, ISSN={1662-5188}, 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.} }