Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17414
Title: Modeling oscillatory phase and phase synchronization with neuronal excitation and input strength in cortical network
Authors: Wang, D
Sun, Y
Wang, F
Li, J
Keywords: Neuronal coherence;neuronal oscillation;pairwise phase consistency PPC;phase synchronization;spike-LFP phase;spiking neural network
Issue Date: 7-Jun-2018
Publisher: IEEE
Citation: IEEE Access, 2018, 6 pp. 36441 - 36458
Abstract: Neuronal oscillatory phase is suggested to be associated with feature coding, carrying information for stimulus identity and neuronal activation, while phase synchronization is indicated to be correlated with signal routing, establishing flexible communication structures for neuronal interactions. Recent electrophysiological and computational studies have revealed that oscillatory phase has close relationships with neuronal excitation and input stimulus. To simulate and further investigate these issues, we simulated orientation columns with a spiking neural network and performed spectral computations according to physiological experiments. Besides, six network activity states, pre-stimulus, and stimulus periods were introduced in our simulation for both independent and comparative analyses. The simulation results demonstrated that gamma band neuronal oscillations existed in the network and even emerged during pre-stimulus period. An input stimulus orientation, if approximately preferred, could produce smaller and more concentrated oscillatory phases, but relatively stronger phase synchronization. In particular, the oscillatory phase and phase synchronization had quantifiable relationships with neuronal excitation and input strength. With the network activity state transforming gradually from strong oscillation to non-oscillation, the oscillatory phase became more and more scattered and the strength of phase synchronization declined significantly. Their relationships with neuronal excitation and input strength became increasingly unstable, and finally collapsed.
URI: http://bura.brunel.ac.uk/handle/2438/17414
DOI: http://dx.doi.org/10.1109/ACCESS.2018.2845301
ISSN: http://dx.doi.org/10.1109/ACCESS.2018.2845301
2169-3536
Appears in Collections:Dept of Computer Science Research Papers

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