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

Anis Yuniati, Te Lun Mai, Chi Ming Chen

Research output: Contribution to journalArticle

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 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.

Original languageEnglish
Article number2
JournalFrontiers in Computational Neuroscience
Volume11
DOIs
Publication statusPublished - 2017 Jan 31

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

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

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