Solving Navigational Uncertainty Using Grid Cells on Robots

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments

Using Strategic Movement to Calibrate a Neural Compass: A Spiking Network for Tracking Head Direction in Rats and Robots

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex

Human spatial representation: what we cannot learn from the studies of rodent navigation

Studies of human and rodent navigation often reveal a remarkable cross-species similarity between the cognitive and neural mechanisms of navigation. Such cross-species resemblance often overshadows some critical differences between how humans and nonhuman animals navigate. In this review, I first argued that a navigation system requires both a storage system (i.e., representing spatial information) and a positioning system (i.e., sensing spatial information) to operate. I then argued that the way humans represent spatial information is different from that inferred from the cellular activity observed during rodent navigation. Such difference spans the whole hierarchy of spatial representation, from representing the structure of environment to the representation of sub-regions of an environment, routes and paths, and the distance and direction relative to a goal location. These cross-species inconsistencies suggested that what we learned from rodent navigation does not always transferable to human navigation. Finally, I argue for closing the loop for the dominant, unidirectional animal-to-human approach in navigation research, so that insights from behavioral studies of human navigation may also flow back to shed light on the cellular mechanisms of navigation for both humans and other mammals (i.e., a human-to-animal approach)

HLA-DR Alpha 2 Mediates Negative Signalling via Binding to Tirc7 Leading to Anti-Inflammatory and Apoptotic Effects in Lymphocytes In Vitro and In Vivo

Classically, HLA-DR expressed on antigen presenting cells (APC) initiates lymphocyte activation via presentation of peptides to TCR bearing CD4+ T-Cells. Here we demonstrate that HLA-DR alpha 2 domain (sHLA-DRα2) also induces negative signals by engaging TIRC7 on lymphocytes. This interaction inhibits proliferation and induces apoptosis in CD4+ and CD8+ T-cells via activation of the intrinsic pathway. Proliferation inhibition is associated with SHP-1 recruitment by TIRC7, decreased phosphorylation of STAT4, TCR-ζ chain & ZAP70, and inhibition of IFN-γ and FasL expression. HLA-DRα2 and TIRC7 co-localize at the APC-T cell interaction site. Triggering HLA-DR - TIRC7 pathway demonstrates that sHLA-DRα2 treatment inhibits proinflammatory-inflammatory cytokine expression in APC & T cells after lipopolysaccaride (LPS) stimulation in vitro and induces apoptosis in vivo. These results suggest a novel antiproliferative role for HLA-DR mediated via TIRC7, revise the notion of an exclusive stimulatory interaction of HLA-DR with CD4+ T cells and highlights a novel physiologically relevant regulatory pathway

Shiga Toxin and Lipopolysaccharide Induce Platelet-Leukocyte Aggregates and Tissue Factor Release, a Thrombotic Mechanism in Hemolytic Uremic Syndrome

BACKGROUND: Aggregates formed between leukocytes and platelets in the circulation lead to release of tissue factor (TF)-bearing microparticles contributing to a prothrombotic state. As enterohemorrhagic Escherichia coli (EHEC) may cause hemolytic uremic syndrome (HUS), in which microthrombi cause tissue damage, this study investigated whether the interaction between blood cells and EHEC virulence factors Shiga toxin (Stx) and lipopolysaccharide (LPS) led to release of TF. METHODOLOGY/PRINCIPAL FINDINGS: The interaction between Stx or LPS and blood cells induced platelet-leukocyte aggregate formation and tissue factor (TF) release, as detected by flow cytometry in whole blood. O157LPS was more potent than other LPS serotypes. Aggregates formed mainly between monocytes and platelets and less so between neutrophils and platelets. Stimulated blood cells in complex expressed activation markers, and microparticles were released. Microparticles originated mainly from platelets and monocytes and expressed TF. TF-expressing microparticles, and functional TF in plasma, increased when blood cells were simultaneously exposed to the EHEC virulence factors and high shear stress. Stx and LPS in combination had a more pronounced effect on platelet-monocyte aggregate formation, and TF expression on these aggregates, than each virulence factor alone. Whole blood and plasma from HUS patients (n = 4) were analyzed. All patients had an increase in leukocyte-platelet aggregates, mainly between monocytes and platelets, on which TF was expressed during the acute phase of disease. Patients also exhibited an increase in microparticles, mainly originating from platelets and monocytes, bearing surface-bound TF, and functional TF was detected in their plasma. Blood cell aggregates, microparticles, and TF decreased upon recovery. CONCLUSIONS/SIGNIFICANCE: By triggering TF release in the circulation, Stx and LPS can induce a prothrombotic state contributing to the pathogenesis of HUS

Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion

A key challenge in visual place recognition (VPR) is recognizing places despite drastic visual appearance changes due to factors such as time of day, season, weather or lighting conditions. Numerous approaches based on deep-learnt image descriptors, sequence matching, domain translation, and probabilistic localization have had success in addressing this challenge, but most rely on the availability of carefully curated representative reference images of the possible places. In this paper, we propose a novel approach, dubbed Bayesian Selective Fusion, for actively selecting and fusing informative reference images to determine the best place match for a given query image. The selective element of our approach avoids the counterproductive fusion of every reference image and enables the dynamic selection of informative reference images in environments with changing visual conditions (such as indoors with flickering lights, outdoors during sunshowers or over the day-night cycle). The probabilistic element of our approach provides a means of fusing multiple reference images that accounts for their varying uncertainty via a novel training-free likelihood function for VPR. On difficult query images from two benchmark datasets, we demonstrate that our approach matches and exceeds the performance of several alternative fusion approaches along with state-of-the-art techniques that are provided with prior (unfair) knowledge of the best reference images. Our approach is well suited for longterm robot autonomy where dynamic visual environments are commonplace since it is training-free, descriptor-agnostic, and complements existing techniques such as sequence matching