{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/170705713","title":"The Utility of Phase Models in Studying Neural Synchronization","arxiv_id":"1707.05713","date":"2017-07-18","proceeding":null,"authors":["Youngmin Park","Stewart Heitmann","G. Bard Ermentrout"],"abstract":"Synchronized neural spiking is associated with many cognitive functions and\nthus, merits study for its own sake. The analysis of neural synchronization\nnaturally leads to the study of repetitive spiking and consequently to the\nanalysis of coupled neural oscillators. Coupled oscillator theory thus informs\nthe synchronization of spiking neuronal networks. A crucial aspect of coupled\noscillator theory is the phase response curve (PRC), which describes the impact\nof a perturbation to the phase of an oscillator. In neural terms, the\nperturbation represents an incoming synaptic potential which may either advance\nor retard the timing of the next spike. The phase response curves and the form\nof coupling between reciprocally coupled oscillators defines the phase\ninteraction function, which in turn predicts the synchronization outcome\n(in-phase versus anti-phase) and the rate of convergence. We review the two\nclasses of PRC and demonstrate the utility of the phase model in predicting\nsynchronization in reciprocally coupled neural models. In addition, we compare\nthe rate of convergence for all combinations of reciprocally coupled Class I\nand Class II oscillators. These findings predict the general synchronization\noutcomes of broad classes of neurons under both inhibitory and excitatory\nreciprocal coupling.","url_abs":"http://arxiv.org/abs/1707.05713v1","url_pdf":"http://arxiv.org/pdf/1707.05713v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"170705713","repo_url":"https://github.com/youngmp/park_heitmann_ermentrout_wiley_2017","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}