no code implementations • 4 Feb 2024 • Linnéa Gyllingberg, Yu Tian, David J. T. Sumpter
We then show that in an oscillatory environment our model builds efficient solutions, provided the environmental oscillations are sufficiently out of phase.
no code implementations • 3 Apr 2023 • Linnéa Gyllingberg, Alex Szorkovszky, David J. T. Sumpter
In this paper, we propose a model of social burst and glide motion by combining a well-studied model of neuronal dynamics, the FitzHugh-Nagumo model, with a model of fish motion.
no code implementations • 19 Jan 2023 • Linnéa Gyllingberg, David J. T. Sumpter, Åke Brännström
We calculate extinction probability for the individual-based model and show convergence between the local approximation and the non-spatial global approximation of the individual-based model as dispersal distance and population size simultaneously tend to infinity.
no code implementations • 19 Jan 2023 • Linnéa Gyllingberg, Abeba Birhane, David J. T. Sumpter
We provide a critique of mathematical biology in light of rapid developments in modern machine learning.