Unpacking the cognitive map: the parallel map theory of hippocampal function

In the parallel map theory, the hippocampus encodes space with 2 mapping systems. The bearing map is constructed primarily in the dentate gyrus from directional cues such as stimulus gradients. The sketch map is constructed within the hippocampus proper from positional cues. The integrated map emerges when data from the bearing and sketch maps are combined. Because the component maps work in parallel, the impairment of one can reveal residual learning by the other. Such parallel function may explain paradoxes of spatial learning, such as learning after partial hippocampal lesions, taxonomic and sex differences in spatial learning, and the function of hippocampal neurogenesis. By integrating evidence from physiology to phylogeny, the parallel map theory offers a unified explanation for hippocampal function.

Fine-tuning the space, time, and host distribution of mycobacteria in wildlife.

Background. We describe the diversity of two kinds of mycobacteria isolates, environmental mycobacteria and Mycobacterium bovis collected from wild boar, fallow deer, red deer and cattle in Doñana National Park (DNP, Spain), analyzing their association with temporal, spatial and environmental factors. Results. High diversity of environmental mycobacteria species and M. bovis typing patterns (TPs) were found. When assessing the factors underlying the presence of the most common types of both environmental mycobacteria and M. bovis TPs in DNP, we evidenced (i) host species differences in the occurrence, (ii) spatial structuration and (iii) differences in the degree of spatial association of specific types between host species. Co-infection of a single host by two M. bovis TPs occurred in all three wild ungulate species. In wild boar and red deer, isolation of one group of mycobacteria occurred more frequently in individuals not infected by the other group. While only three TPs were detected in wildlife between 1998 and 2003, up to 8 different ones were found during 2006-2007. The opposite was observed in cattle. Belonging to an M. bovis-infected social group was a significant risk factor for mycobacterial infection in red deer and wild boar, but not for fallow deer. M. bovis TPs were usually found closer to water marshland than MOTT. Conclusions. The diversity of mycobacteria described herein is indicative of multiple introduction events and a complex multi-host and multi-pathogen epidemiology in DNP. Significant changes in the mycobacterial isolate community may have taken place, even in a short time period (1998 to 2007). Aspects of host social organization should be taken into account in wildlife epidemiology. Wildlife in DNP is frequently exposed to different species of non-tuberculous, environmental mycobacteria, which could interact with the immune response to pathogenic mycobacteria, although the effects are unknown. This research highlights the suitability of molecular typing for surveys at small spatial and temporal scales.

Dissociation of allocentric and egocentric orientation: Effect on hippocampal place cells and place navigation

Navigation by means of cognitive maps appears to require the hippocampus; hippocampal place cells (PCs) appear to store spatial memories because their discharge is confined to cell-specific places called firing fields (FFs). Experiments with rats manipulated idiothetic and landmark-related information to understand the relationship between PC activity and spatial rotation. Rotating a circular arena in the caused a discrepancy between these cuse. This discrepancy caused most FFs to disappear in both the arena and room reference frames. However, FFs persisted in the rotating arena frame when the discrepancy was reduced by darkness or by a card in the arena. The discrepancy was increased by "field clamping" the rat in a room-defined FF location by rotations that countered its locomotion. Most FFs disspared and reappeared an hour or more after the clamp. Place-avoidance experiments showed that navigation uses independent idiothetic and exteroceptive memories. Rats learned to avoid the unmarked footshock region within a circular arena. When acquired on the stable arena in the light, the location of the punishment was learned by using both room and idiothetic cues; extinction in the dark transferred to the following session in the light. If, however, extinction occured during rotation, only the arena-frame avoidance was extinguished in darkness; the room-defined location was avoided when the light were turned back on. Idiothetic memory of room-defined avoidance was not formed during rotation in light; regardless of rotation with a randomly dispersed pellet. The resulting behaviour alternated between random pellet searching and target-directed navigation, making it possible to examine PC correlates of these two classes of spatial behaviour. The independence of idiothetic and exteroceptive spatial memories and the disruption of PC firing during rotation suggest that PCs may not be necessary for spatial cognition; this idea can be tested by recording during place-avoidance and preference tasks.

Use of high-resolution geophysical data to characterize heterogeneous aquifers: influence of data integration method on hydrological predictions

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. The investigation of exactly how much benefit can be brought by geophysical data in terms of its effect on hydrological predictions, however, has received considerably less attention in the literature. Here, we examine the potential hydrological benefits brought by a recently introduced simulated annealing (SA) conditional stochastic simulation method designed for the assimilation of diverse hydrogeophysical data sets. We consider the specific case of integrating crosshole ground-penetrating radar (GPR) and borehole porosity log data to characterize the porosity distribution in saturated heterogeneous aquifers. In many cases, porosity is linked to hydraulic conductivity and thus to flow and transport behavior. To perform our evaluation, we first generate a number of synthetic porosity fields exhibiting varying degrees of spatial continuity and structural complexity. Next, we simulate the collection of crosshole GPR data between several boreholes in these fields, and the collection of porosity log data at the borehole locations. The inverted GPR data, together with the porosity logs, are then used to reconstruct the porosity field using the SA-based method, along with a number of other more elementary approaches. Assuming that the grid-cell-scale relationship between porosity and hydraulic conductivity is unique and known, the porosity realizations are then used in groundwater flow and contaminant transport simulations to assess the benefits and limitations of the different approaches.

A framework for assessing the scale of influence of environmental factors on ecological patterns

The distribution of living organisms, habitats and ecosystems is primarily driven by abiotic environmental factors that are spatially structured. Assessing the spatial structure of environmental factors, e.g., through spatial autocorrelation analyses (SAC), can thus help us understand their scale of influence on the distribution of organisms, habitats, and ecosystems. Yet SAC analyses of environmental factors are still rarely performed in biogeographic studies. Here, we describe a novel framework that combines SAC and statistical clustering to identify scales of spatial patterning of environmental factors, which can then be interpreted as the scales at which those factors influence the geographic distribution of biological and ecological features. We illustrate this new framework with datasets at different spatial or thematic resolutions. This framework is conceptually and statistically robust, providing a valuable approach to tackle a wide range of issues in ecological and environmental research and particularly when building predictors for ecological models. The new framework can significantly promote fundamental research on all spatially-structured ecological patterns. It can also foster research and application in such fields as global change ecology, conservation planning, and landscape management.

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R² = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R² = 0.97), 2 (R² = 0.60), 3 (R² = 0.63) and 4 (R² = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.

Fine-tuning the space, time, and host distribution of mycobacteria in wildlife.

BACKGROUND We describe the diversity of two kinds of mycobacteria isolates, environmental mycobacteria and Mycobacterium bovis collected from wild boar, fallow deer, red deer and cattle in Doñana National Park (DNP, Spain), analyzing their association with temporal, spatial and environmental factors. RESULTS High diversity of environmental mycobacteria species and M. bovis typing patterns (TPs) were found. When assessing the factors underlying the presence of the most common types of both environmental mycobacteria and M. bovis TPs in DNP, we evidenced (i) host species differences in the occurrence, (ii) spatial structuration and (iii) differences in the degree of spatial association of specific types between host species. Co-infection of a single host by two M. bovis TPs occurred in all three wild ungulate species. In wild boar and red deer, isolation of one group of mycobacteria occurred more frequently in individuals not infected by the other group. While only three TPs were detected in wildlife between 1998 and 2003, up to 8 different ones were found during 2006-2007. The opposite was observed in cattle. Belonging to an M. bovis-infected social group was a significant risk factor for mycobacterial infection in red deer and wild boar, but not for fallow deer. M. bovis TPs were usually found closer to water marshland than MOTT. CONCLUSIONS The diversity of mycobacteria described herein is indicative of multiple introduction events and a complex multi-host and multi-pathogen epidemiology in DNP. Significant changes in the mycobacterial isolate community may have taken place, even in a short time period (1998 to 2007). Aspects of host social organization should be taken into account in wildlife epidemiology. Wildlife in DNP is frequently exposed to different species of non-tuberculous, environmental mycobacteria, which could interact with the immune response to pathogenic mycobacteria, although the effects are unknown. This research highlights the suitability of molecular typing for surveys at small spatial and temporal scales.

Regional consumption of antibiotics : a demand system approach

Through this paper. we have attempted to model the demand for different classes of antibiotics used for respiratory infections in outpatient care in Switzerland using a spatial version of the linear approximate Almost Ideal Demand System (AIDS) model. This model takes spatial dependency into account by means of spatial lags of antibiotic budget shares. We control for the health status of patients and the potential harmful effects of antibiotic use in terms of bacterial resistance. Elasticities to socioeconomic determinants of consumption and own- and cross-price elasticities between different groups of antibiotic have also been computed in this paper. Significant cross-price elasticities are found between newer or more expensive generations and older or less expensive generations of antibiotics. (C) 2009 Elsevier B.V. All rights reserved.

Spatial learning by rats across visually disconnected environments

Experiments were designed to examine some properties of spatial representations in rats. Adult subjects were trained to escape through a hole at a fixed position in a large circular arena (see Schenk 1989). The experiments were conducted in the dark, with a limited number of controlled visual light cues in order to assess the minimal cue requirement for place learning. Three identical light cues (shape, height and distance from the table) were used. Depending on the condition, they were either permanently on, or alternatively on or off, depending on the position of the rat in the field. Two questions were asked: a) how many identical visual cues were necessary for spatial discrimination in the dark, and b) could rats integrate the relative positions of separate cues, under conditions in which the rat was never allowed to perceive all three cues simultaneously. The results suggest that rats are able to achieve a place discrimination task even if the three cues necessary for efficient orientation can never be seen simultaneously. A dissociation between the discrimination of the spatial position of the goal and the capacity to reach it by a direct path suggests that a reduced number of cues might require prolonged locomotion to allow an accurate orientation in the environment.

Indirect effects of peri-and postnatal choline treatment on place-learning abilities in rat.

This work was aimed at analyzing the effects of perinatal choline supplementation on the development of spatial abilities and upon adult performance. Choline supplementation (3.5 g/L in 0.02 M saccharin solution in tap water) was maintained for two weeks before birth and for up to four weeks postnatally. Additional supplementation was maintained from the fifth to the tenth week postnatally. Spatial-learning capacities were studied at the ages of 26, 65, or 80 days in a circular swimming pool (Morris place-navigation task) and at the age of 7 months in a homing arena. Treatment effects were found in both juvenile and adult rats, and thus persisted for several months after the cessation of the supplementation. The choline supplementation improved the performance in the water maze in a very selective manner. The most consistent effect was a reduction in the latency to reach a cued platform at a fixed position in space, whereas the improvement was limited when the platform was invisible and had to be located relative to distant cues only. However, after removal of the goal cue, the treated rats showed a better retention of the training position than did the control rats. A similar effect was observed in a dry-land task conducted in the homing arena. The choline supplementation thus induced a significant improvement of spatial memory. But since this effect was only evident following training with a salient cue, it might be regarded as an indirect effect promoted by an optimal combination of cue guidance with a place strategy.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

What spatial data do we need to develop global mammal conservation strategies?

Spatial data on species distributions are available in two main forms, point locations and distribution maps (polygon ranges and grids). The first are often temporally and spatially biased, and too discontinuous, to be useful (untransformed) in spatial analyses. A variety of modelling approaches are used to transform point locations into maps. We discuss the attributes that point location data and distribution maps must satisfy in order to be useful in conservation planning. We recommend that before point location data are used to produce and/or evaluate distribution models, the dataset should be assessed under a set of criteria, including sample size, age of data, environmental/geographical coverage, independence, accuracy, time relevance and (often forgotten) representation of areas of permanent and natural presence of the species. Distribution maps must satisfy additional attributes if used for conservation analyses and strategies, including minimizing commission and omission errors, credibility of the source/assessors and availability for public screening. We review currently available databases for mammals globally and show that they are highly variable in complying with these attributes. The heterogeneity and weakness of spatial data seriously constrain their utility to global and also sub-global scale conservation analyses.

Sex differences in the computation of traveling distance

Path integration is known to provide information to keep track of spatial location. Surprisingly, few investigations concerning sex differences in computation of the traveling distance have been done. This work was aimed at analyzing the reproduction of both passive and active linear displacements in women and men. To this end, the displacement of blindfolded subjects was done in a wheelchair, then on foot, three times in each condition for a fixed distance. Copies of passive and active traveling distance, distance estimations and pointing responses towards the starting point were analyzed. In passive condition and comparatively to men, women error was larger. Whereas traveling distance was generally underestimated in women, it was overestimated in men. In active condition, no sex differences were observed. When blindfolded subjects have to estimate the traveling distance, the female error was larger than the male one. But, when subjects were asked to indicate the visual cue corresponding to the traveling distance, the male error was larger than the female one. Finally, pointing to the starting point (0°) after a whole-body rotation showed a larger deviation from 0° in men than in women. These results suggest that sex of the subjects influence brain computation of path integration information.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.