Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Altered decision-making in multiple sclerosis: a sign of impaired emotional reactivity?

We assessed decision-making capacity and emotional reactivity in 20 patients with multiple sclerosis (MS) and in 16 healthy subjects using the Gambling Task (GT), a model of real-life decision making, and the skin conductance response (SCR). Demographic, neurological, affective, and cognitive parameters were analyzed in MS patients for their effect on decision-making performance. MS patients persisted longer (slope, -3.6%) than the comparison group (slope, -6.4%) in making disadvantageous choices as the GT progressed (p < 0.001), suggesting significant slower learning in MS. Patients with higher Expanded Disability Status Scale scores (EDSS >2.0) showed a different pattern of impairment in the learning process compared with patients with lower functional impairment (EDSS </=2.0). This slower learning was associated with impaired emotional reactivity (anticipatory SCR 3.9 vs 6.1 microSiemens [microS] for patients vs the comparison group, p < 0.0001; post-choice SCR 3.9 vs 6.2 microS, p < 0.0001), but not with executive dysfunction. Impaired emotional dimensions of behavior (assessed using the Dysexecutive Questionnaire, p < 0.002) also correlated with slower learning. Given the considerable consequences that impaired decision making can have on daily life, we suggest that this factor may contribute to handicap and altered quality of life secondary to MS and is dependent on emotional experience. Ann Neurol 2004.

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.

Optogenetic Interrogation of Dopaminergic Modulation of the Multiple Phases of Reward-Seeking Behavior.

Phasic activation of dopaminergic neurons is associated with reward-predicting cues and supports learning during behavioral adaptation. While noncontingent activation of dopaminergic neurons in the ventral tegmental are (VTA) is sufficient for passive behavioral conditioning, it remains unknown whether the phasic dopaminergic signal is truly reinforcing. In this study, we first targeted the expression of channelrhodopsin-2 to dopaminergic neurons of the VTA and optimized optogenetically evoked dopamine transients. Second, we showed that phasic activation of dopaminergic neurons in freely moving mice causally enhances positive reinforcing actions in a food-seeking operant task. Interestingly, such effect was not found in the absence of food reward. We further found that phasic activation of dopaminergic neurons is sufficient to reactivate previously extinguished food-seeking behavior in the absence of external cues. This was also confirmed using a single-session reversal paradigm. Collectively, these data suggest that activation of dopaminergic neurons facilitates the development of positive reinforcement during reward-seeking and behavioral flexibility.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.