Dr Brian Gardner

Feature binding - how the brain encodes which features are part of other features to form representations of the coherent objects we perceive - remains an unsolved problem in neuroscience. Despite progress towards a solution, major theories either lack detailed explanations at the neuronal level or rely on biologically unrealistic simplifications, and none adequately account for the representation of hierarchical information, which is crucial to our perception of the world. To address this, a solution termed binding by polychrony has been proposed to explain how hierarchical feature relationships may be encoded at the neuronal level in a biologically realistic system. This theory relies on a phenomenon known as polychronization, where groups of neurons fire in precisely coordinated, time-locked sequences, leading to the emergence of regularly repeating spatiotemporal patterns that might encode these relationships. In this study, we explore binding by polychrony through simulations of a spiking neural network that closely aligns with the structural organisation of the primate ventral visual pathway, incorporating bottom-up, top-down, and lateral synaptic connections. By exposing the network to collections of related 2D object shapes from ecologically realistic datasets and applying spike-timing-dependent plasticity, the network self-organises such that individual neurons respond selectively to specific shape features. Furthermore, the network exhibits polychronization, giving rise to repeating spatiotemporal patterns, some of which form circuits that encode hierarchical feature relationships. Notably, these circuits are robust, even with the randomised, Poisson-distributed spike timings that represent the visual stimuli in the input layer. These results provide evidence for binding by polychrony as a feasible solution to the feature binding problem, and characterise the mechanism by which it may function. This mechanism can guide experimentalists in identifying such circuits in vivo, and could also be utilised in computer vision systems to capture more information and improve robustness to adversarial inputs.

Gut microbiota are essential for maintaining host health, for example by providing protection against pathogens. This has prompted numerous studies to explore the composition and diversity of microbial communities using metagenomics techniques. Although this allows some degree of insight, there remains a shortcoming in understanding the interactions and temporal dynamics of these communities in greater detail. Moreover, this knowledge gap becomes further pronounced as we also consider the impact of external perturbations, for example antibiotic treatment, on the long-term stability of these microbial communities. To address this, we developed a mechanistic modelling framework based on the generalised Lotka–Volterra model to predict microbial compositional changes over time and to assess its sensitivity to applied external perturbation. Essentially, whether or not the microbial community bounces back to its original configuration once the perturbation has stopped. Using in-silico data and publicly available data derived from 16S rRNA sequencing, we estimated microbial growth rates and their mutual interactions. This model relies on absolute abundance counts, which can be estimated from total microbial biomass measurements, and the data is organised into the topmost abundant taxa organised at the genus level to prevent over-fitting. Bayesian inference was used to estimate model parameters from these abundance counts. Perturbations were represented by imposing seasonal changes in microbial growth rates that fluctuated about their non-perturbed values. After fitting the model to these datasets, we explored different applications of the perturbation signal and evaluated its impact on the long-term stability of the community dynamics. The model shows that, even if the intensity of the perturbation is the same (e.g., a given dosage of antibiotics), there are specific frequencies at which the perturbation is administered that can cause pronounced responses (resonance). Essentially, we can induce large deviations in microbial abundances from their equilibrium values and even drive some taxa to extinction. Applications of this approach include identifying optimal antibiotic treatment regimens to minimise the emergence of superbugs resistant to antibiotics and informing personalised strategies for maintaining a healthy gut microbiota.

Background: Antibiotic resistance increasingly threatens the interconnected health of humans, animals, and the environment. While misuse of antibiotics is a known driver, environmental factors also play a critical role. A balanced One Health approach—including the environmental sector—is necessary to understand the emergence and spread of resistance. Methods: We systematically searched English-language literature (1990–2021) in MEDLINE, Embase, and Web of Science, plus grey literature. Titles, abstracts, and keywords were screened, followed by full-text reviews using a structured codebook and dual-reviewer assessments. Results: Of 13,667 records screened, 738 met the inclusion criteria. Most studies focused on freshwater and terrestrial environments, particularly associated with wastewater or manure sources. Evidence of research has predominantly focused on Escherichia coli and Pseudomonas spp., with a concentration on ARGs conferring resistance to sulphonamides (sul1–3), tetracyclines (tet), and beta-lactams. Additionally, the People’s Republic of China has produced a third of the studies—twice that of the next country, the United States—and research was largely domestic, with closely linked author networks. Conclusion: Significant evidence gaps persist in understanding antibiotic resistance in non-built environments, particularly in marine, atmospheric, and non-agricultural set65 tings. Stressors such as climate change and microplastics remain notably under-explored. There is also an urgent need for more research in low-income regions, which face higher risks of antibiotic resistance, to support the development of targeted, evidence-based interventions.

The gut microbiota play a key role in the health of animals and humans. However, the dynamic properties and stability of the microbiota are poorly understood. We propose a regression technique for parameter inference of a mechanistic model to describe the temporal dynamics of these microbial communities. The model could be used for measuring community resilience against external perturbing factors, such as antibiotic therapy.