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Title: A Group Analysis of Oscillatory Phase and Phase Synchronization in Cortical Networks
Authors: Wang, D
Sun, Y
Shi, H
Wang, F
Keywords: Neuronal oscillation;Phase synchronization;Spike-LFP phase;Pairwise phase consistency for group PPCG;Neuronal coherence;Neuronal assembly;Spiking neural network
Issue Date: 3-Mar-2020
Publisher: IEEE
Citation: D. Wang, Y. Sun, H. Shi and F. Wang, "A Group Analysis of Oscillatory Phase and Phase Synchronization in Cortical Networks," in IEEE Access, vol. 8, pp. 59182-59199, 2020,
Abstract: Neuronal oscillatory phase and phase synchronization are two main aspects of neuronal oscillation. Neurophysiological and computational studies have demonstrated that oscillatory phase for individual neurons has quantifiable relationships with neuronal excitation and input stimulus. In order to investigate the issue for neuronal groups, we constructed orientation columns by means of a spiking neural network and introduced six network activity states, pre-stimulus and stimulus periods for comparison. We proposed a new method of spike-LFP (Local Field Potential) phase based on vector addition of point spike-LFP phases to represent oscillatory phase. We also proposed a PPCG (Pairwise Phase Consistency for Group) method to quantify phase synchronization for neuronal groups. As illustrated in the simulation, the characteristics of oscillatory phase and phase synchronization for neuronal groups were consistent with the ones for individual neurons. Preferred orientations and stronger external inputs tended to result in smaller and more concentrated oscillatory phases. No matter individual neurons or neuronal groups, the oscillatory phase decreased monotonically as a function of neuronal excitation and input strength. More importantly, neuronal groups had a competitive advantage over individual neurons, because they can achieve reliable relationship quantification of oscillatory phase for all network activity states, even in weak oscillatory or non-oscillatory states.
ISSN: 2169-3536
Appears in Collections:Dept of Computer Science Research Papers

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