Allen Cheung

The information content of panoramic images I: The rotational errors and the similarity of views in rectangular experimental arenas

Animals relocating a target corner in a rectangular space often make rotational errors searching not only at the target corner but also at the diagonally opposite corner. The authors tested whether view-based navigation can explain rotational errors by recording panoramic snapshots at regularly spaced locations in a rectangular box. The authors calculated the global image difference between the image at each location and the image recorded at a target location in 1 of the corners, thus creating a 2-dimensional map of image differences. The authors found the most pronounced minima of image differences at the target corner and the diagonally opposite corner--conditions favoring rotational errors. The authors confirmed these results in virtual reality simulations and showed that the relative salience of different visual cues determines whether image differences are dominated by geometry or by features. The geometry of space is thus implicitly contained in panoramic images and does not require explicit computation by a dedicated module. A testable prediction is that animals making rotational errors in rectangular spaces are guided by remembered views.

The Information Content of Panoramic Images II: View-Based Navigation in Nonrectangular Experimental Arenas

Two recent studies testing navigation of rats in swimming pools have posed problems for any account of the use of purely geometric properties of space in navigation (M. Graham, M. A. Good, A. McGregor, & J. M. Pearce, 2006; J. M. Pearce, M. A. Good, P. M. Jones, & A. McGregor, 2004). The authors simulated 1 experiment from each study in a virtual reality environment to test whether experimental results could be explained by view-based navigation. The authors recorded a reference image at the target location and then determined global panoramic image differences between this image and images taken at regularly spaced locations throughout the arena. A formal model, in which an agent attempts to minimize image differences between the reference image and current views, generated trajectories that could be compared with the search performance of rats. For both experiments, this model mimics many aspects of rat behavior. View-based navigation provides a sufficient and parsimonious explanation for a range of navigational behaviors of rats under these experimental conditions.

Animal navigation: the difficulty of moving in a straight line

In principle, there are two strategies for navigating a straight course. One is to use an external directional reference and continually reorienting with reference to it, while the other is to infer body rotations from internal sensory information only. We show here that, while the first strategy will enable an animal or mobile agent to move arbitrarily far away from its starting point, the second strategy will not do so, even after an infinite number of steps. Thus, an external directional reference—some form of compass—is indispensable for ensuring progress away from home. This limitation must place significant constraints on the evolution of biological navigation systems. Some specific examples are discussed. An important corollary arising from the analysis of compassless navigation is that the maximum expected displacement represents a robust measure of the straightness of a path.

Human cytomegalovirus latent infection and associated viral gene expression

Human cytomegalovirus (HCMV) is a clinically important and ubiquitous herpesvirus. Following primary productive infection the virus is not completely eliminated from the host, but instead establishes a lifelong latent infection without detectable virus production, from where it can reactivate at a later stage to generate new infectious virus. Reactivated HCMV often results in life-threatening disease in immunocompromised individuals, particularly allogeneic stem cell and solid organ transplant recipients, where it remains one of the most difficult opportunistic pathogens that complicate the care of these patients. The ability of HCMV to establish and reactivate from latency is central to its success as a human pathogen, yet latency remains very poorly understood. This article will cover several aspects of HCMV latency, with a focus on current understanding of viral gene expression and functions during this phase of infection.

Viral Interleukin-10 Expressed by Human Cytomegalovirus during the Latent Phase of Infection Modulates Latently Infected Myeloid Cell Differentiation†

ABSTRACT The human cytomegalovirus UL111A gene is expressed during latent and productive infections, and it codes for homologs of interleukin-10 (IL-10). We examined whether viral IL-10 expressed during latency altered differentiation of latently infected myeloid progenitors. In comparison to infection with parental virus or mock infection, latent infection with a virus in which the gene encoding viral IL-10 has been deleted upregulated cytokines associated with dendritic cell (DC) formation and increased the proportion of myeloid DCs. These data demonstrate that viral IL-10 restricts the ability of latently infected myeloid progenitors to differentiate into DCs and identifies an immunomodulatory role for viral IL-10 which may limit the hosts ability to clear latent virus.

Which coordinate system for modelling path integration

Path integration is a navigation strategy widely observed in nature where an animal maintains a running estimate, called the home vector, of its location during an excursion. Evidence suggests it is both ancient and ubiquitous in nature, and has been studied for over a century. In that time, canonical and neural network models have flourished, based on a wide range of assumptions, justifications and supporting data. Despite the importance of the phenomenon, consensus and unifying principles appear lacking. A fundamental issue is the neural representation of space needed for biological path integration. This paper presents a scheme to classify path integration systems on the basis of the way the home vector records and updates the spatial relationship between the animal and its home location. Four extended classes of coordinate systems are used to unify and review both canonical and neural network models of path integration, from the arthropod and mammalian literature. This scheme demonstrates analytical equivalence between models which may otherwise appear unrelated, and distinguishes between models which may superficially appear similar. A thorough analysis is carried out of the equational forms of important facets of path integration including updating, steering, searching and systematic errors, using each of the four coordinate systems. The type of available directional cue, namely allothetic or idiothetic, is also considered. It is shown that on balance, the class of home vectors which includes the geocentric Cartesian coordinate system, appears to be the most robust for biological systems. A key conclusion is that deducing computational structure from behavioural data alone will be difficult or impossible, at least in the absence of an analysis of random errors. Consequently it is likely that further theoretical insights into path integration will require an in-depth study of the effect of noise on the four classes of home vectors.

Structural perturbations within Ge nanocrystals in silica

Extended x-ray absorption fine structure (EXAFS) spectroscopy was used to identify structural perturbations in Ge nanocrystals produced in silica by ion implantation and annealing. Although the nanocrystals retained tetrahedral coordination, both the short- and medium-range orders were perturbed relative to bulk crystalline material. Equivalently, the nanocrystal interatomic distance distribution deviated from that of bulk crystalline Ge, exhibiting enhanced structural disorder of both Gaussian and non-Gaussian forms in the first, second, and third nearest-neighbor shells. The relative influences of nanocrystal size, bonding distortions, multiple phases, and a matrix-induced compression were considered.

Maintaining a cognitive map in darkness: the need to fuse boundary knowledge with path integration.

Spatial navigation requires the processing of complex, disparate and often ambiguous sensory data. The neurocomputations underpinning this vital ability remain poorly understood. Controversy remains as to whether multimodal sensory information must be combined into a unified representation, consistent with Tolmans “cognitive map”, or whether differential activation of independent navigation modules suffice to explain observed navigation behaviour. Here we demonstrate that key neural correlates of spatial navigation in darkness cannot be explained if the path integration system acted independently of boundary (landmark) information. In vivo recordings demonstrate that the rodent head direction (HD) system becomes unstable within three minutes without vision. In contrast, rodents maintain stable place fields and grid fields for over half an hour without vision. Using a simple HD error model, we show analytically that idiothetic path integration (iPI) alone cannot be used to maintain any stable place representation beyond two to three minutes. We then use a measure of place stability based on information theoretic principles to prove that featureless boundaries alone cannot be used to improve localization above chance level. Having shown that neither iPI nor boundaries alone are sufficient, we then address the question of whether their combination is sufficient and – we conjecture – necessary to maintain place stability for prolonged periods without vision. We addressed this question in simulations and robot experiments using a navigation model comprising of a particle filter and boundary map. The model replicates published experimental results on place field and grid field stability without vision, and makes testable predictions including place field splitting and grid field rescaling if the true arena geometry differs from the acquired boundary map. We discuss our findings in light of current theories of animal navigation and neuronal computation, and elaborate on their implications and significance for the design, analysis and interpretation of experiments.

Honeybee flight: A novel ‘streamlining’ response

SUMMARY Animals that move rapidly through the air can save considerable energy by reducing the drag that they need to overcome during flight. We describe a novel ‘streamlining’ response in tethered, flying honeybees in which the abdomen is held in a raised position when the visual system is exposed to a pattern of image motion that is characteristic of forward flight. This visually evoked response, which can be elicited without exposing the insect to any airflow, presumably serves to reduce the aerodynamic drag that would otherwise be produced by the abdomen during real flight. The response is critically dependent on the presence of appropriate image motion everywhere within the large field of view of the insect. Thus, our results also underscore the importance of using panoramic stimulation for the study of visually guided flight in insects, and reveal the relative importance of various regions of the visual field in assessing the speed of flight through the environment.

Finding the way with a noisy brain

Successful navigation is fundamental to the survival of nearly every animal on earth, and achieved by nervous systems of vastly different sizes and characteristics. Yet surprisingly little is known of the detailed neural circuitry from any species which can accurately represent space for navigation. Path integration is one of the oldest and most ubiquitous navigation strategies in the animal kingdom. Despite a plethora of computational models, from equational to neural network form, there is currently no consensus, even in principle, of how this important phenomenon occurs neurally. Recently, all path integration models were examined according to a novel, unifying classification system. Here we combine this theoretical framework with recent insights from directed walk theory, and develop an intuitive yet mathematically rigorous proof that only one class of neural representation of space can tolerate noise during path integration. This result suggests many existing models of path integration are not biologically plausible due to their intolerance to noise. This surprising result imposes significant computational limitations on the neurobiological spatial representation of all successfully navigating animals, irrespective of species. Indeed, noise-tolerance may be an important functional constraint on the evolution of neuroarchitectural plans in the animal kingdom.