Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Organization and plasticity of auditory spatial representations

Spatial hearing refers to a set of abilities enabling us to determine the location of sound sources, redirect our attention toward relevant acoustic events, and recognize separate sound sources in noisy environments. Determining the location of sound sources plays a key role in the way in which humans perceive and interact with their environment. Deficits in sound localization abilities are observed after lesions to the neural tissues supporting these functions and can result in serious handicaps in everyday life. These deficits can, however, be remediated (at least to a certain degree) by the surprising capacity of reorganization that the human brain possesses following damage and/or learning, namely, the brain plasticity. In this thesis, our aim was to investigate the functional organization of auditory spatial functions and the learning-induced plasticity of these functions. Overall, we describe the results of three studies. The first study entitled "The role of the right parietal cortex in sound localization: A chronometric single pulse transcranial magnetic stimulation study" (At et al., 2011), study A, investigated the role of the right parietal cortex in spatial functions and its chronometry (i.e. the critical time window of its contribution to sound localizations). We concentrated on the behavioral changes produced by the temporarily inactivation of the parietal cortex with transcranial magnetic stimulation (TMS). We found that the integrity of the right parietal cortex is crucial for localizing sounds in the space and determined a critical time window of its involvement, suggesting a right parietal dominance for auditory spatial discrimination in both hemispaces. In "Distributed coding of the auditory space in man: evidence from training-induced plasticity" (At et al., 2013a), study B, we investigated the neurophysiological correlates and changes of the different sub-parties of the right auditory hemispace induced by a multi-day auditory spatial training in healthy subjects with electroencephalography (EEG). We report a distributed coding for sound locations over numerous auditory regions, particular auditory areas code specifically for precise parts of the auditory space, and this specificity for a distinct region is enhanced with training. In the third study "Training-induced changes in auditory spatial mismatch negativity" (At et al., 2013b), study C, we investigated the pre-attentive neurophysiological changes induced with a training over 4 days in healthy subjects with a passive mismatch negativity (MMN) paradigm. We showed that training changed the mechanisms for the relative representation of sound positions and not the specific lateralization themselves and that it changed the coding in right parahippocampal regions. - L'audition spatiale désigne notre capacité à localiser des sources sonores dans l'espace, de diriger notre attention vers les événements acoustiques pertinents et de reconnaître des sources sonores appartenant à des objets distincts dans un environnement bruyant. La localisation des sources sonores joue un rôle important dans la façon dont les humains perçoivent et interagissent avec leur environnement. Des déficits dans la localisation de sons sont souvent observés quand les réseaux neuronaux impliqués dans cette fonction sont endommagés. Ces déficits peuvent handicaper sévèrement les patients dans leur vie de tous les jours. Cependant, ces déficits peuvent (au moins à un certain degré) être réhabilités grâce à la plasticité cérébrale, la capacité du cerveau humain à se réorganiser après des lésions ou un apprentissage. L'objectif de cette thèse était d'étudier l'organisation fonctionnelle de l'audition spatiale et la plasticité induite par l'apprentissage de ces fonctions. Dans la première étude intitulé « The role of the right parietal cortex in sound localization : A chronometric single pulse study » (At et al., 2011), étude A, nous avons examiné le rôle du cortex pariétal droit dans l'audition spatiale et sa chronométrie, c'est-à- dire le moment critique de son intervention dans la localisation de sons. Nous nous sommes concentrés sur les changements comportementaux induits par l'inactivation temporaire du cortex pariétal droit par le biais de la Stimulation Transcrânienne Magnétique (TMS). Nous avons démontré que l'intégrité du cortex pariétal droit est cruciale pour localiser des sons dans l'espace. Nous avons aussi défini le moment critique de l'intervention de cette structure. Dans « Distributed coding of the auditory space : evidence from training-induced plasticity » (At et al., 2013a), étude B, nous avons examiné la plasticité cérébrale induite par un entraînement des capacités de discrimination auditive spatiale de plusieurs jours. Nous avons montré que le codage des positions spatiales est distribué dans de nombreuses régions auditives, que des aires auditives spécifiques codent pour des parties données de l'espace et que cette spécificité pour des régions distinctes est augmentée par l'entraînement. Dans « Training-induced changes in auditory spatial mismatch negativity » (At et al., 2013b), étude C, nous avons examiné les changements neurophysiologiques pré- attentionnels induits par un entraînement de quatre jours. Nous avons montré que l'entraînement modifie la représentation des positions spatiales entraînées et non-entrainées, et que le codage de ces positions est modifié dans des régions parahippocampales.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Dissociation between components of spatial memory in rats after recovery from the effects of retrohippocampal lesions

A series of 4 experiments examined the performance of rats with retrohippocampal lesions on a spatial water-maze task. The animals were trained to find and escape onto a hidden platform after swimming in a large pool of opaque water. The platform was invisible and could not be located using olfactory cues. Successful escape performance required the rats to develop strategies of approaching the correct location with reference solely to distal extramaze cues. The lesions encompassed the entire rostro-caudal extent of the lateral and medial entorhinal cortex, and included parts of the pre- and para-subiculum, angular bundle and subiculum. Groups ECR 1 and 2 sustained only partial damage of the subiculum, while Group ECR+S sustained extensive damage. These groups were compared with sham-lesion and unoperated control groups. In Expt 1A, a profound deficit in spatial localisation was found in groups ECR 1 and ECR+S, the rats receiving all training postoperatively. In Expt 1B, these two groups showed hyperactivity in an open-field. In Expt 2, extensive preoperative training caused a transitory saving in performance of the spatial task by group ECR 2, but comparisons with the groups of Expt 1A revealed no sustained improvement, except on one measure of performance in a post-training transfer test. All rats were then given (Expt 3) training on a cueing procedure using a visible platform. The spatial deficit disappeared but, on returning to the normal hidden platform procedure, it reappeared. Nevertheless, a final transfer test, during which the platform was removed from the apparatus, revealed a dissociation between two independent measures of performance: the rats with ECR lesions failed to search for the hidden platform but repeatedly crossed its correct location accurately during traverses of the entire pool. This partial recovery of performance was not (Expt 4) associated with any ability to discriminate between two locations in the pool. The apparently selective recovery of aspects of spatial memory is discussed in relation to O'Keefe and Nadel's (1978) spatial mapping theory of hippocampal function. We propose a modification of the theory in terms of a dissociation between procedural and declarative subcomponents of spatial memory. The declarative component is a flexible access system in which information is stored in a form independent of action. It is permanently lost after the lesion. The procedural component is "unmasked" by the retrohippocampal lesion giving rise to the partial recovery of spatial localisation performance.

Gambling against neglect: unconscious spatial biases induced by reward reinforcement in healthy people and brain-damaged patients.

Orienting attention in space recruits fronto-parietal networks whose damage results in unilateral spatial neglect. However, attention orienting may also be governed by emotional and motivational factors; but it remains unknown whether these factors act through a modulation of the fronto-parietal attentional systems or distinct neural pathways. Here we asked whether attentional orienting is affected by learning about the reward value of targets in a visual search task, in a spatially specific manner, and whether these effects are preserved in right-brain damaged patients with left spatial neglect. We found that associating rewards with left-sided (but not right-sided) targets during search led to progressive exploration biases towards left space, in both healthy people and neglect patients. Such spatially specific biases occurred even without any conscious awareness of the asymmetric reward contingencies. These results show that reward-induced modulations of space representation are preserved despite a dysfunction of fronto-parietal networks associated with neglect, and therefore suggest that they may arise through spared subcortical networks directly acting on sensory processing and/or oculomotor circuits. These effects could be usefully exploited for potentiating rehabilitation strategies in neglect patients.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Effects of caffeine on learning and memory in rats tested in the Morris water maze

We studied some of the characteristics of the improving effect of the non-specific adenosine receptor antagonist, caffeine, using an animal model of learning and memory. Groups of 12 adult male Wistar rats receiving caffeine (0.3-30 mg/kg, ip, in 0.1 ml/100 g body weight) administered 30 min before training, immediately after training, or 30 min before the test session were tested in the spatial version of the Morris water maze task. Post-training administration of caffeine improved memory retention at the doses of 0.3-10 mg/kg (the rats swam up to 600 cm less to find the platform in the test session, P<=0.05) but not at the dose of 30 mg/kg. Pre-test caffeine administration also caused a small increase in memory retrieval (the escape path of the rats was up to 500 cm shorter, P<=0.05). In contrast, pre-training caffeine administration did not alter the performance of the animals either in the training or in the test session. These data provide evidence that caffeine improves memory retention but not memory acquisition, explaining some discrepancies among reports in the literature.

The effects of estimating good vs. poor knowledge of results during acquisition of a spatial motor task

Recent studies have shown that providing learners Knowledge of Results (KR) after “good trials” rather than “poor trials” is superior for learning. The present study examined whether requiring participants to estimate their three best or three worst trials in a series of six trial blocks before receiving KR would prove superior to learning compared to not estimating their performance. Participants were required to push and release a slide along a confined pathway using their non-dominant hand to a target distance (133cm). The retention and transfer data suggest those participants who received KR after good trials demonstrated superior learning and performance estimations compared to those receiving KR after poor trials. The results of the present experiment offer an important theoretical extension in our understanding of the role of KR content and performance estimation on motor skill learning.

Learning Three-Dimensional Shape Models for Sketch Recognition

Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.

The spatial scan statistic

Learning object 38 additional resource

Occupational therapy and its relevance in social projects. relationships, learning and opportunities: collection of experiences of University of Alberta Mscot students in Bogotá, Colombia

Occupational therapists are equipped to promote wellbeing through occupation and to enable participation and meaningful engagement of people in their social and physical environments (WFOT, 2012). As such, the role of the occupational therapists is profoundly linked to the social, cultural and environmental characteristics of the contexts in which occupations take place. The central role that context plays in occupational performance creates an interesting dichotomy for the occupational therapist: on one hand, a profound understanding of cultural and social factors is required from the Occupational Therapy (OT) in order to develop a meaningful and successful collaboration with the person; on the other hand, the ability of the occupational therapists to recognize and explore the contextual factor of an occupation-person dyad transcends cultural and spatial barriers. As a result, occupational therapists are equipped to engage in international collaboration and practice, and as such face unique and enriching challenges. International fieldwork experiences have become a tool through which occupational therapists in training can develop the critical skills for understanding the impact of cultural and social factors on occupation. An OT student in an international fieldwork experience faces numerous challenges in leading a process that is both relevant and respectful to the characteristics of the local context: language, cultural perceptions of occupation and personhood, religious backgrounds, health care access, etc. These challenges stand out as ethical considerations that must be considered when navigating an international fieldwork experience (AOTA, 2009). For more than five years now, the Faculty of Rehabilitation Medicine (FRM) of the University of Alberta (UoFA) and the School of Medicine and Health Sciences at the Universidad del Rosario (UR), Bogota, Colombia, have sustained a productive and meaningful international collaboration. This collaboration includes a visit by Dr. Albert Cook, professor of the FRM and former dean, to the UR as the main guest speaker in the International Congress of Technologies for Disability Support (IBERDISCAP) in 2008. Furthermore, Dr. Cook was a speaker in the research seminar of the Assistive Technology Research Group of the Universidad del Rosario. Following Dr. Cook’s visit, Professors Liliana Álvarez and Adriana Ríos travelled to Edmonton and initiated collaboration with the FRM, resulting in the signing of an agreement between the FRM and the UR in 2009, agreement that has been maintained to this day. The main goal of this agreement is to increase academic and cultural cooperation between the UR and the UofA. Other activities have included the cooperation between Dr. Kim Adams (who has largely maintained interest and effort in supporting the capacity building of the UR rehabilitation programs in coordinating the provision of research placement opportunities for UR students at the UofA), an Assistive Technology course for clinicians and students led by Dr. Adams, and a research project that researched the use of basic cell phones to provide social interaction and health information access for people with disabilities in a low-income community in Colombia (led by Tim Barlott, OT, MSc, under the supervision of Dr. Adams). Since the beginning, the occupational therapy programs of the Universidad del Rosario and the University of Alberta have promoted this collaboration and have strived to engage in interactions that provide further development opportunities for students and staff. As part of this process, the international placement experience of UofA OT students was born under the leadership of: Claudia Rozo, OT program director at UR, placement and academic leadership of Elvis Castro and Angélica Monsalve, professors of the occupational therapy program at UR; and Dr. Lili Liu, OT department director at UofA, Cori Schmitz, Academic coordinator of clinical education at the UofA; and Tim Barlott and Liliana Álvarez leading the international and cross-cultural aspect of this collaboration.This publication summarizes and illustrates the process of international placement in community settings in Colombia, undertaken by occupational therapy students of the University of Alberta. It is our hope that this document can provide and document the ethical considerations of international fieldwork experience, the special characteristics of communities and the ways in which cultural and social competences are developed and help international students navigate the international setting. We also hope that this document will stimulate discussion among professional and academic communities about the importance and richness of international placement experiences and encourage staff and students to articulate their daily efforts with the global occupational therapy agenda.

Location, location, location: development of spatiotemporal sequence learning in infancy

We investigated infants' sensitivity to spatiotemporal structure. In Experiment 1, circles appeared in a statistically defined spatial pattern. At test 11-month-olds, but not 8-month-olds, looked longer at a novel spatial sequence. Experiment 2 presented different color/shape stimuli, but only the location sequence was violated during test; 8-month-olds preferred the novel spatial structure, but 5-month-olds did not. In Experiment 3, the locations but not color/shape pairings were constant at test; 5-month-olds showed a novelty preference. Experiment 4 examined "online learning": We recorded eye movements of 8-month-olds watching a spatiotemporal sequence. Saccade latencies to predictable locations decreased. We argue that temporal order statistics involving informative spatial relations become available to infants during the first year after birth, assisted by multiple cues.

Learning associations between places and visual cues without learning to navigate: neither fornix nor entorhinal cortex is required

Rats with fornix transection, or with cytotoxic retrohippocampal lesions that removed entorhinal cortex plus ventral subiculum, performed a task that permits incidental learning about either allocentric (Allo) or egocentric (Ego) spatial cues without the need to navigate by them. Rats learned eight visual discriminations among computer-displayed scenes in a Y-maze, using the constant-negative paradigm. Every discrimination problem included two familiar scenes (constants) and many less familiar scenes (variables). On each trial, the rats chose between a constant and a variable scene, with the choice of the variable rewarded. In six problems, the two constant scenes had correlated spatial properties, either Alto (each constant appeared always in the same maze arm) or Ego (each constant always appeared in a fixed direction from the start arm) or both (Allo + Ego). In two No-Cue (NC) problems, the two constants appeared in randomly determined arms and directions. Intact rats learn problems with an added Allo or Ego cue faster than NC problems; this facilitation provides indirect evidence that they learn the associations between scenes and spatial cues, even though that is not required for problem solution. Fornix and retrohippocampal-lesioned groups learned NC problems at a similar rate to sham-operated controls and showed as much facilitation of learning by added spatial cues as did the controls; therefore, both lesion groups must have encoded the spatial cues and have incidentally learned their associations with particular constant scenes. Similar facilitation was seen in subgroups that had short or long prior experience with the apparatus and task. Therefore, neither major hippocampal input-output system is crucial for learning about allocentric or egocentric cues in this paradigm, which does not require rats to control their choices or navigation directly by spatial cues.

Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon

Developing high-quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini-reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth-atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub-disciplines, increase the temporal and spatial scale of existing studies, and improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large-scale interdisciplinary questions. Bringing together a diverse scientific community with a single geographic focus can help increase awareness of research questions both within and among disciplines, and reveal the opportunities that may exist for advancing acquisition of reliable knowledge. This approach could be useful for a variety of globally important scientific questions.