Literature DB >> 9961005

Clustering and slow switching in globally coupled phase oscillators.

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Abstract

Year:  1993        PMID： 9961005     DOI： 10.1103/physreve.48.3470

Source DB:  PubMed          Journal:  Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics        ISSN： 1063-651X


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  30 in total

1.  Stability of two cluster solutions in pulse coupled networks of neural oscillators.

Authors:  Lakshmi Chandrasekaran; Srisairam Achuthan; Carmen C Canavier
Journal:  J Comput Neurosci       Date:  2010-08-20       Impact factor: 1.621

2.  Mathematical Frameworks for Oscillatory Network Dynamics in Neuroscience.

Authors:  Peter Ashwin; Stephen Coombes; Rachel Nicks
Journal:  J Math Neurosci       Date:  2016-01-06       Impact factor: 1.300

3.  On the stationary state of a network of inhibitory spiking neurons.

Authors:  Wolfgang Kinzel
Journal:  J Comput Neurosci       Date:  2007-07-13       Impact factor: 1.621

4.  Design principles for phase-splitting behaviour of coupled cellular oscillators: clues from hamsters with 'split' circadian rhythms.

Authors:  Premananda Indic; William J Schwartz; David Paydarfar
Journal:  J R Soc Interface       Date:  2008-08-06       Impact factor: 4.118

5.  Desynchronization in networks of globally coupled neurons with dendritic dynamics.

Authors:  Milan Majtanik; Kevin Dolan; Peter A Tass
Journal:  J Biol Phys       Date:  2006-11-10       Impact factor: 1.365

6.  Reducing Neuronal Networks to Discrete Dynamics.

Authors:  David Terman; Sungwoo Ahn; Xueying Wang; Winfried Just
Journal:  Physica D       Date:  2008-03       Impact factor: 2.300

7.  The role of axonal delay in the synchronization of networks of coupled cortical oscillators.

Authors:  S M Crook; G B Ermentrout; M C Vanier; J M Bower
Journal:  J Comput Neurosci       Date:  1997-04       Impact factor: 1.621

8.  Emergent spike patterns in neuronal populations.

Authors:  Logan Chariker; Lai-Sang Young
Journal:  J Comput Neurosci       Date:  2014-10-18       Impact factor: 1.621

9.  Phase response theory explains cluster formation in sparsely but strongly connected inhibitory neural networks and effects of jitter due to sparse connectivity.

Authors:  Ruben A Tikidji-Hamburyan; Conrad A Leonik; Carmen C Canavier
Journal:  J Neurophysiol       Date:  2019-02-06       Impact factor: 2.714

10.  Phase response theory extended to nonoscillatory network components.

Authors:  Fred H Sieling; Santiago Archila; Ryan Hooper; Carmen C Canavier; Astrid A Prinz
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2012-05-14
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