Zobrazeno 1 - 10
of 227
pro vyhledávání: '"Laing, Carlo R."'
Autor:
Omel'chenko, Oleh E., Laing, Carlo R.
We consider a ring network of quadratic integrate-and-fire neurons with nonlocal synaptic and gap junction coupling. The corresponding neural field model supports solutions such as standing and travelling waves, and also lurching waves. We show that
Externí odkaz:
http://arxiv.org/abs/2406.01881
Autor:
Laing, Carlo R., Omel'chenko, Oleh E.
We consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location
Externí odkaz:
http://arxiv.org/abs/2306.10398
Autor:
Laing, Carlo R., Krauskopf, Bernd
We consider a single theta neuron with delayed self-feedback in the form of a Dirac delta function in time. Because the dynamics of a theta neuron on its own can be solved explicitly -- it is either excitable or shows self-pulsations -- we are able t
Externí odkaz:
http://arxiv.org/abs/2205.03904
Autor:
Laing, Carlo R.
Chimeras occur in networks of two coupled populations of oscillators when the oscillators in one population synchronise while those in the other are asynchronous. We consider chimeras of this form in networks of planar oscillators for which one param
Externí odkaz:
http://arxiv.org/abs/2201.12491
Publikováno v:
In Communications in Nonlinear Science and Numerical Simulation April 2024 131
Large collections of coupled, heterogeneous agents can manifest complex dynamical behavior presenting difficulties for simulation and analysis. However, if the collective dynamics lie on a low-dimensional manifold then the original agent-based model
Externí odkaz:
http://arxiv.org/abs/2105.09398
Autor:
Laing, Carlo R.
We consider large networks of theta neurons and use the Ott/Antonsen ansatz to derive degree-based mean field equations governing the expected dynamics of the networks. Assuming random connectivity we investigate the effects of varying the widths of
Externí odkaz:
http://arxiv.org/abs/2104.14666
Autor:
Kemeth, Felix P., Bertalan, Tom, Thiem, Thomas, Dietrich, Felix, Moon, Sung Joon, Laing, Carlo R., Kevrekidis, Ioannis G.
We extract data-driven, intrinsic spatial coordinates from observations of the dynamics of large systems of coupled heterogeneous agents. These coordinates then serve as an emergent space in which to learn predictive models in the form of partial dif
Externí odkaz:
http://arxiv.org/abs/2012.12738
In this paper we present a systematic, data-driven approach to discovering "bespoke" coarse variables based on manifold learning algorithms. We illustrate this methodology with the classic Kuramoto phase oscillator model, and demonstrate how our mani
Externí odkaz:
http://arxiv.org/abs/2004.06053
Autor:
Laing, Carlo R., Omel'chenko, Oleh
Publikováno v:
Chaos 30, 043117 (2020)
We consider large networks of theta neurons on a ring, synaptically coupled with an asymmetric kernel. Such networks support stable "bumps" of activity, which move along the ring if the coupling kernel is asymmetric. We investigate the effects of the
Externí odkaz:
http://arxiv.org/abs/2004.00699