Learning-induced synchronization and plasticity of a developing neural network

Journal of Computational Neuroscience
T C Chao, C M Chen

Abstract

Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.

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Citations

Apr 15, 2014·PloS One·Jérémie Cabessa, Alessandro E P Villa
Jul 29, 2009·Neural Plasticity·Yanhua Ruan, Gang Zhao
Dec 21, 2011·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·I-H LinC-M Chen
Feb 16, 2017·Frontiers in Computational Neuroscience·Anis YuniatiChi-Ming Chen

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