AUTHOR=Yuniati Anis , Mai Te-Lun , 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/journals/computational-neuroscience/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.