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DC Field | Value | Language |
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dc.contributor.author | Wang, D | - |
dc.contributor.author | Sun, Y | - |
dc.contributor.author | Wang, F | - |
dc.contributor.author | Li, J | - |
dc.date.accessioned | 2019-01-23T15:36:04Z | - |
dc.date.available | 2018-06-06 | - |
dc.date.available | 2019-01-23T15:36:04Z | - |
dc.date.issued | 2018-06-07 | - |
dc.identifier.citation | Wang, D., Sun, Y., Wang, F. and Li, J. (2018) 'Modeling Oscillatory Phase and Phase Synchronization With Neuronal Excitation and Input Strength in Cortical Network,' IEEE Access, 6, pp. 36441 - 36458. doi: 10.1109/ACCESS.2018.2845301. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/17414 | - |
dc.description.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. | en_US |
dc.description.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China; 10.13039/100007219-Natural Science Foundation of Shanghai; Fundamental Research Funds for Central Universities; | - |
dc.format.extent | 36441 - 36458 | - |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.rights | This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles are currently published under Creative Commons licenses (either CCBY or CCBY-NC-ND), and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles published under CCBY, or use them for any other lawful purpose, as long as proper attribution is given. Articles published under CCBY-NC-ND are also available to users under the same conditions as CCBY, but the reuse cannot be for commercial purposes or change the work in any way. | - |
dc.subject | neuronal coherence | en_US |
dc.subject | neuronal oscillation | en_US |
dc.subject | pairwise phase consistency PPC | en_US |
dc.subject | phase synchronization | en_US |
dc.subject | spike-LFP phase | en_US |
dc.subject | spiking neural network | en_US |
dc.title | Modeling oscillatory phase and phase synchronization with neuronal excitation and input strength in cortical network | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/ACCESS.2018.2845301 | - |
dc.relation.isPartOf | IEEE Access | - |
pubs.publication-status | Published | - |
pubs.volume | 6 | - |
dc.identifier.eissn | 2169-3536 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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Fulltext.pdf | 1.42 MB | Adobe PDF | View/Open |
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