The Army of One (Sample): the Characteristics of Sampling-based Probabilistic Neural Representations

There is growing evidence that humans and animals represent the uncertainty associated with sensory stimuli and utilize this uncertainty during planning and decision making in a statistically optimal way. Recently, a nonparametric framework for representing probabilistic information has been proposed whereby neural activity encodes samples from the distribution over external variables. Although such sample-based probabilistic representations have strong empirical and theoretical support, two major issues need to be clarified before they can be considered as viable candidate theories of cortical computation. First, in a fluctuating natural environment, can neural dynamics provide sufficient samples to accurately estimate a stimulus? Second, can such a code support accurate learning over biologically plausible time-scales? Although it is well known that sampling is statistically optimal if the number of samples is unlimited, biological constraints mean that estimation and learning in the cortex must be supported by a relatively small number of possibly dependent samples. We explored these issues in a cue combination task by comparing a neural circuit that employed a sampling-based representation to an optimal estimator. For static stimuli, we found that a single sample is sufficient to obtain an estimator with less than twice the optimal variance, and that performance improves with the inverse square root of the number of samples. For dynamic stimuli, with linear-Gaussian evolution, we found that the efficiency of the estimation improves significantly as temporal information stabilizes the estimate, and because sampling does not require a burn-in phase. Finally, we found that using a single sample, the dynamic model can accurately learn the parameters of the input neural populations up to a general scaling factor, which disappears for modest sample size. These results suggest that sample-based representations can support estimation and learning using a relatively small number of samples and are therefore highly feasible alternatives for performing probabilistic cortical computations.
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Learning complex tasks with probabilistic population codes

Recent psychophysical experiments imply that the brain employs a neural representation of the uncertainty in sensory stimuli and that probabilistic computations are supported by the cortex. Several candidate neural codes for uncertainty have been posited including Probabilistic Population Codes (PPCs). PPCs support various versions of probabilistic inference and marginalisation in a neurally plausible manner. However, in order to establish whether PPCs can be of general use, three important limitations must be addressed. First, it is critical that PPCs support learning. For example, during cue combination, subjects are able to learn the uncertainties associated with the sensory cues as well as the prior distribution over the stimulus. However, previous modelling work with PPCs requires these parameters to be carefully set by hand. Second, PPCs must be able to support inference in non-linear models. Previous work has focused on linear models and it is not clear whether non-linear models can be implemented in a neurally plausible manner. Third, PPCs must be shown to scale to high-dimensional problems with many variables. This contribution addresses these three limitations of PPCs by establishing a connection with variational Expectation Maximisation (vEM). In particular, we show that the usual PPC update for cue combination can be interpreted as the E-Step of a vEM algorithm. The corresponding M-Step then automatically provides a method for learning the parameters of the model by adapting the connection strengths in the PPC network in an unsupervised manner. Using a version of sparse coding as an example, we show that the vEM interpretation of PPC can be extended to non-linear and multi-dimensional models and we show how the approach scales with the dimensionality of the problem. Our results provide a rigorous assessment of the ability of PPCs to capture the probabilistic computations performed in the cortex.
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Precise half-life measurement of the 10 h isomer in 154Tb

The precise knowledge of the half-life of the reaction product is of crucial importance for a nuclear reaction cross section measurement carried out with the activation technique. The cross section of the 151Eu(alpha,n)154Tb reaction has been measured recently using the activation method, however, the half-life of the 10 h isomer in 154Tb has a relatively high uncertainty and ambiguous values can be found in the literature. Therefore, the precise half-life of the isomeric state has been measured and found to be 9.994 h +- 0.039 h. With careful analysis of the systematic errors, the uncertainty of this half-life value has been significantly reduced.Comment: Accepted for publication in Nuclear Physics

Statistical Consequences of Devroye Inequality for Processes. Applications to a Class of Non-Uniformly Hyperbolic Dynamical Systems

In this paper, we apply Devroye inequality to study various statistical estimators and fluctuations of observables for processes. Most of these observables are suggested by dynamical systems. These applications concern the co-variance function, the integrated periodogram, the correlation dimension, the kernel density estimator, the speed of convergence of empirical measure, the shadowing property and the almost-sure central limit theorem. We proved in \cite{CCS} that Devroye inequality holds for a class of non-uniformly hyperbolic dynamical systems introduced in \cite{young}. In the second appendix we prove that, if the decay of correlations holds with a common rate for all pairs of functions, then it holds uniformly in the function spaces. In the last appendix we prove that for the subclass of one-dimensional systems studied in \cite{young} the density of the absolutely continuous invariant measure belongs to a Besov space.Comment: 33 pages; companion of the paper math.DS/0412166; corrected version; to appear in Nonlinearit

Resource degradation, marginalization, and poverty in small-scale fisheries: threats to social-ecological resilience in India and Brazil

In this study we examine poverty in local fisheries using a social-ecological resilience lens. In assessing why “fishery may rhyme with poverty”, Christophe Béné suggests a typology of impoverishment processes, which includes economic exclusion, social marginalization, class exploitation, and political disempowerment as key mechanisms that accelerate poverty. We extend his analysis by exploring these four mechanisms further and by intertwining them with processes of environmental change and degradation. Our goal is to understand poverty in local fisheries as a process rooted in social and institutional factors as influenced by ecological dynamics. We argue that understanding poverty will require a focus on the social-ecological system (SES) as a whole, and addressing poverty will mean rebuilding not only collapsed stocks but the entire social-ecological system, including restoring relationships between resources and people. Information from two cases, the Chilika Lagoon on the Bay of Bengal in India, and the Paraty region on the southeastern coast of Brazil, is used to understand how fishery social-ecological systems come under pressure from drivers at multiple levels, resulting in a range of impacts and pushing the system to a breaking point or collapse. We analyze elements of what it takes for the whole system to break down or collapse and push fishers into poverty and marginalization. The Chilika SES has already broken down, and the Paraty SES is under pressure from multiple drivers of change. The two cases help contrast key dynamics in the social, cultural, economic, political, and environmental spheres, for lessons on system collapse and recovery. Rebuilding fisheries may be examined as a process of building and strengthening resilience. The challenge is to make the fishery social-ecological system more resilient, with more flexibility and options, not only within fishing activities but also within a range of other sectors

Snake prices and crocodile appetites: Aquatic wildlife supply and demand on Tonle Sap Lake, Cambodia

Commercial trade is a major driver of over-exploitation of wild species, but the pattern of demand and how it responds to changes in supply is poorly understood. Here we explore the markets for snakes from Tonle Sap Lake in Cambodia to evaluate future exploitation scenarios, identify entry points for conservation and, more generally, to illustrate the value of multi-scale analysis of markets to traded wildlife conservation. In Cambodia, the largest driver of snake exploitation is the domestic trade in snakes as crocodile food. We estimate that farmed crocodiles consume between 2.7 and 12.2 million snakes per year. The market price for crocodiles has been in decline since 2003, which, combined with rising prices for their food, has led to a reduced frequency of feeding and closure of small farms. The large farms that generate a disproportionate amount of the demand for snakes continue to operate in anticipation of future market opportunities, and preferences for snakes could help maintain demand if market prices for crocodiles rise to pre 2003 levels. In the absence of a sustained demand from crocodile farms, it is also possible that alternative markets will develop, such as one for human snack food. The demand for snakes, however, also depends on the availability of substitute resources, principally fish. The substitutability and low price elasticity of demand offers a relatively sustainable form of consumerism. Given the nature of these market drivers, addressing consumer preferences and limiting the protection of snakes to their breeding season are likely to be the most effective tools for conservation. This study highlights the importance of understanding the structure of markets and the behaviour of consumer demand prior to implementing regulations on wildlife hunting and trade

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

Natural reforestation of European mountain landscapes raises major environmental and societal issues. With local stakeholders in the Pyrenees National Park area (France), we studied agricultural landscape colonisation by ash (Fraxinus excelsior) to enlighten its impacts on biodiversity and other landscape functions of importance for the valley socio-economics. The study comprised an integrated assessment of land-use and land-cover change (LUCC) since the 1950s, and a scenario analysis of alternative future policy. We combined knowledge and methods from landscape ecology, land change and agricultural sciences, and a set of coordinated field studies to capture interactions and feedback in the local landscape/land-use system. Our results elicited the hierarchically-nested relationships between social and ecological processes. Agricultural change played a preeminent role in the spatial and temporal patterns of LUCC. Landscape colonisation by ash at the parcel level of organisation was merely controlled by grassland management, and in fact depended on the farmer's land management at the whole-farm level. LUCC patterns at the landscape level depended to a great extent on interactions between farm household behaviours and the spatial arrangement of landholdings within the landscape mosaic. Our results stressed the need to represent the local SES function at a fine scale to adequately capture scenarios of change in landscape functions. These findings orientated our modelling choices in the building an agent-based model for LUCC simulation (SMASH - Spatialized Multi-Agent System of landscape colonization by ASH). We discuss our method and results with reference to topical issues in interdisciplinary research into the sustainability of multifunctional landscapes

A new framework to enable equitable outcomes: resilience and nexus approaches combined

Managing integrated social-ecological systems to reduce risks to human and environmental well-being remains challenging in light of the rate and extent of undesirable changes that are occurring. Developing frameworks that are sufficiently integrative to guide research to deliver the necessary insights into all key system aspects is an important outstanding task. Among existing approaches, resilience and nexus framings both allow focus on unpacking relationships across scales and levels in a system and emphasize the involvement of different groups in decision making to different extents. They also suffer weaknesses and neither approach puts social justice considerations explicitly at its core. This has important implications for understanding who wins and loses out from different decisions and how social and ecological risks and trade-offs are shared and distributed, temporally and spatially. This paper conceptually integrates resilience and nexus approaches, developing a combined framework and indicating how it could effectively be operationalized in cases from mountain and mangrove social-ecological systems. In doing so, it advances understanding of complex social-ecological systems framings for risk-based decision making beyond that which could be achieved through use of either resilience or nexus approaches alone. Important next steps in testing the framework involve empirical and field operationalization, requiring interdisciplinary, mixed method approache