A minimal model of cognition based on oscillatory and reinforcement processes

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.

Precision at the indistinguishability threshold: a method for evaluating classification algorithms

no code implementations • 19 Nov 2023 • David J. T. Sumpter

At this decision threshold, the set of positively labelled images are indistinguishable from the set of images which are positive.

Using neuronal models to capture burst and glide motion and leadership in fish

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.

Finding analytical approximations for discrete, stochastic, individual-based models of ecology

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.

The Lost Art of Mathematical Modelling

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.