We consider a pair of identical theta neurons in the active regime, each coupled to the other via a delayed Dirac delta function. The network can support periodic solutions and we concentrate on solutions for which the neurons are half a period out of phase with one another, and also solutions for which the neurons are perfectly synchronous. The dynamics are analytically solvable, so we can derive explicit expressions for the existence and stability of both types of solutions. We find two branches of solutions, connected by symmetry-broken solutions which arise when the period of a solution as a function of delay is at a maximum or a minimum.

We conduct a theoretical analysis of the performance of $\beta $-encoders. The $\beta $-encoders are A/D (analogue-to-digital) encoders, the design of which is based on the expansion of real numbers with noninteger radix. For the practical use of such encoders, it is important to have theoretical upper bounds of their errors. We investigate the generating function of the Perron–Frobenius operator of the corresponding one-dimensional map and deduce the invariant measure of it. Using this, we derive an approximate value of the upper bound of the mean squared error of the quantization process of such encoders. We also discuss the results from a numerical viewpoint.

Quorum sensing governs bacterial communication, playing a crucial role in regulating population behaviour. We propose a mathematical model that uncovers chaotic dynamics within quorum sensing networks, highlighting challenges to predictability. The model explores interactions between autoinducers and two bacterial subtypes, revealing oscillatory dynamics in both a constant autoinducer submodel and the full three-component model. In the latter case, we find that the complicated dynamics can be explained by the presence of homoclinic Shilnikov bifurcations. We employ a combination of normal-form analysis and numerical continuation methods to analyse the system.

We study the planar FitzHugh–Nagumo system with an attracting periodic orbit that surrounds a repelling focus equilibrium. When the associated oscillation of the system is perturbed, in a given direction and with a given amplitude, there will generally be a change in phase of the perturbed oscillation with respect to the unperturbed one. This is recorded by the phase transition curve (PTC), which relates the old phase (along the periodic orbit) to the new phase (after perturbation). We take a geometric point of view and consider the phase-resetting surface comprising all PTCs as a function of the perturbation amplitude. This surface has a singularity when the perturbation maps a point on the periodic orbit exactly onto the repelling focus, which is the only point that does not return to stable oscillation. We also consider the PTC as a function of the direction of the perturbation and present how the corresponding phase-resetting surface changes with increasing perturbation amplitude. In this way, we provide a complete geometric interpretation of how the PTC changes for any perturbation direction. Unlike other examples discussed in the literature so far, the FitzHugh–Nagumo system is a generic example and, hence, representative for planar vector fields.

For multi-scale differential equations (or fast–slow equations), one often encounters problems in which a key system parameter slowly passes through a bifurcation. In this article, we show that a pair of prototypical reaction–diffusion equations in two space dimensions can exhibit delayed Hopf bifurcations. Solutions that approach attracting/stable states before the instantaneous Hopf point stay near these states for long, spatially dependent times after these states have become repelling/unstable. We use the complex Ginzburg–Landau equation and the Brusselator models as prototypes. We show that there exist two-dimensional spatio-temporal buffer surfaces and memory surfaces in the three-dimensional space-time. We derive asymptotic formulas for them for the complex Ginzburg–Landau equation and show numerically that they exist also for the Brusselator model. At each point in the domain, these surfaces determine how long the delay in the loss of stability lasts, that is, to leading order when the spatially dependent onset of the post-Hopf oscillations occurs. Also, the onset of the oscillations in these partial differential equations is a hard onset.

The shimmy oscillations of a truck’s front wheels with dependent suspension are studied to investigate how shimmy depends on changes in inflation pressure, with emphasis on the inclusion of four nonlinear tyre characteristics to improve the accuracy of the results. To this end, a three degree-of-freedom shimmy model is created which reflects pressure dependency initially only through tyre lateral force. Bifurcation analysis of the model reveals that four Hopf bifurcations are found with decreased pressures, corresponding to two shimmy modes: the yaw and the tramp modes, and there is no intersection between them. Hopf bifurcations disappear at pressures slightly above nominal value, resulting in a system free of shimmy. Further, two-parameter continuations illustrate that there are two competitive mechanisms between the four pressure-dependent tyre properties, suggesting that the shimmy model should balance these competing factors to accurately capture the effects of pressure. Therefore, the mathematical relations between these properties and inflation pressure are introduced to extend the initial model. Bifurcation diagrams computed on the initial and extended models are compared, showing that for pressures below nominal value, shimmy is aggravated as the two modes merge and the shimmy region expands, but for higher pressures, shimmy is mitigated and disappears early.

The Jansen–Rit model of a cortical column in the cerebral cortex is widely used to simulate spontaneous brain activity (electroencephalogram, EEG) and event-related potentials. It couples a pyramidal cell population with two interneuron populations, of which one is fast and excitatory, and the other slow and inhibitory.

Our paper studies the transition between alpha and delta oscillations produced by the model. Delta oscillations are slower than alpha oscillations and have a more complex relaxation-type time profile. In the context of neuronal population activation dynamics, a small threshold means that neurons begin to activate with small input or stimulus, indicating high sensitivity to incoming signals. A steep slope signifies that activation increases sharply as input crosses the threshold. Accordingly, in the model, the excitatory activation thresholds are small and the slopes are steep. Hence, we replace the excitatory activation function with its singular limit, which is an all-or-nothing switch (a Heaviside function). In this limit, we identify the transition between alpha and delta oscillations as a discontinuity-induced grazing bifurcation. At the grazing, the minimum of the pyramidal-cell output equals the threshold for switching off the excitatory interneuron population, leading to a collapse in excitatory feedback.

We show that passively mode-locked lasers, subject to feedback from a single external cavity can exhibit large timing fluctuations on short time scales, despite having a relatively small long-term timing jitter. This means that the commonly used von Linde and Kéfélian techniques of experimentally estimating the timing jitter can lead to large errors in the estimation of the arrival time of pulses. We also show that adding a second feedback cavity of the appropriate length can significantly suppress noise-induced modulations that are present in the single feedback system. This reduces the short time-scale fluctuations of the interspike interval time and, at the same time, improves the variance of the fluctuation of the pulse arrival times on long time scales.

We present a method for reconstructing evolutionary trees from high-dimensional data, with a specific application to bird song spectrograms. We address the challenge of inferring phylogenetic relationships from phenotypic traits, like vocalizations, without predefined acoustic properties. Our approach combines two main components: Poincaré embeddings for dimensionality reduction and distance computation, and the neighbour-joining algorithm for tree reconstruction. Unlike previous work, we employ Siamese networks to learn embeddings from only leaf node samples of the latent tree. We demonstrate our method’s effectiveness on both synthetic data and spectrograms from six species of finches.