Saved in:
Bibliographic Details
Main Authors: Omel'chenko, Oleh E., Laing, Carlo R.
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.22113
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866917431739416576
author Omel'chenko, Oleh E.
Laing, Carlo R.
author_facet Omel'chenko, Oleh E.
Laing, Carlo R.
contents We study networks of theta neurons arranged on a ring with delayed interactions. In the continuum limit the systems are described by next generation neural field models with delays. We consider distributed delays with both finite and infinite support, and conduction delays. The stability of spatially uniform and localized bump states is determined, and we find that they undergo Hopf bifurcations as parameters related to the delays are varied. These bifurcations create traveling waves and ``breathing'' bump solutions. These dynamic solutions satisfy self-consistency equations and we show how to efficiently solve these equations. Following traveling waves and periodic solutions as parameters are varied provides a global picture of the influence of different delays on pattern formation processes in spatially extended networks of theta neurons.
format Preprint
id arxiv_https___arxiv_org_abs_2604_22113
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Dynamic solutions of next generation neural field models with delays
Omel'chenko, Oleh E.
Laing, Carlo R.
Pattern Formation and Solitons
We study networks of theta neurons arranged on a ring with delayed interactions. In the continuum limit the systems are described by next generation neural field models with delays. We consider distributed delays with both finite and infinite support, and conduction delays. The stability of spatially uniform and localized bump states is determined, and we find that they undergo Hopf bifurcations as parameters related to the delays are varied. These bifurcations create traveling waves and ``breathing'' bump solutions. These dynamic solutions satisfy self-consistency equations and we show how to efficiently solve these equations. Following traveling waves and periodic solutions as parameters are varied provides a global picture of the influence of different delays on pattern formation processes in spatially extended networks of theta neurons.
title Dynamic solutions of next generation neural field models with delays
topic Pattern Formation and Solitons
url https://arxiv.org/abs/2604.22113