Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics

Modelling survival : exposure pattern, species sensitivity and uncertainty

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans

Using quantile regression to investigate racial disparities in medication non-adherence

<p>Abstract</p> <p>Background</p> <p>Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups.</p> <p>Methods</p> <p>A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).</p> <p>Results</p> <p>Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.</p> <p>Conclusion</p> <p>Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.</p

Using Evolutionary Algorithms for Fitting High-Dimensional Models to Neuronal Data

In the study of neurosciences, and of complex biological systems in general, there is frequently a need to fit mathematical models with large numbers of parameters to highly complex datasets. Here we consider algorithms of two different classes, gradient following (GF) methods and evolutionary algorithms (EA) and examine their performance in fitting a 9-parameter model of a filter-based visual neuron to real data recorded from a sample of 107 neurons in macaque primary visual cortex (V1). Although the GF method converged very rapidly on a solution, it was highly susceptible to the effects of local minima in the error surface and produced relatively poor fits unless the initial estimates of the parameters were already very good. Conversely, although the EA required many more iterations of evaluating the model neuron’s response to a series of stimuli, it ultimately found better solutions in nearly all cases and its performance was independent of the starting parameters of the model. Thus, although the fitting process was lengthy in terms of processing time, the relative lack of human intervention in the evolutionary algorithm, and its ability ultimately to generate model fits that could be trusted as being close to optimal, made it far superior in this particular application than the gradient following methods. This is likely to be the case in many further complex systems, as are often found in neuroscience

Rapid submarine melting of the calving faces of West Greenland glaciers

Widespread glacier acceleration has been observed in Greenland in the past few years associated with the thinning of the lower reaches of the glaciers as they terminate in the ocean. These glaciers thin both at the surface, from warm air temperatures, and along their submerged faces in contact with warm ocean waters. Little is known about the rates of submarine melting and how they may affect glacier dynamics. Here we present measurements of ocean currents, temperature and salinity near the calving fronts of the Eqip Sermia, Kangilerngata Sermia, Sermeq Kujatdleq and Sermeq Avangnardleq glaciers in central West Greenland, as well as ice-front bathymetry and geographical positions. We calculate water-mass and heat budgets that reveal summer submarine melt rates ranging from 0.7±0.2 to 3.9±0.8 m d -1. These rates of submarine melting are two orders of magnitude larger than surface melt rates, but comparable to rates of iceberg discharge. We conclude that ocean waters melt a considerable, but highly variable, fraction of the calving fronts of glaciers before they disintegrate into icebergs, and suggest that submarine melting must have a profound influence on grounding-line stability and ice-flow dynamics. © 2010 Macmillan Publishers Limited. All rights reserved

Stressed but Stable: Canopy Loss Decreased Species Synchrony and Metabolic Variability in an Intertidal Hard-Bottom Community

The temporal stability of aggregate community properties depends on the dynamics of the component species. Since species growth can compensate for the decline of other species, synchronous species dynamics can maintain stability (i.e. invariability) in aggregate properties such as community abundance and metabolism. In field experiments we tested the separate and interactive effects of two stressors associated with storminess–loss of a canopy-forming species and mechanical disturbances–on species synchrony and community respiration of intertidal hard-bottom communities on Helgoland Island, NE Atlantic. Treatments consisted of regular removal of the canopy-forming seaweed Fucus serratus and a mechanical disturbance applied once at the onset of the experiment in March 2006. The level of synchrony in species abundances was assessed from estimates of species percentage cover every three months until September 2007. Experiments at two sites consistently showed that canopy loss significantly reduced species synchrony. Mechanical disturbance had neither separate nor interactive effects on species synchrony. Accordingly, in situ measurements of CO2-fluxes showed that canopy loss, but not mechanical disturbances, significantly reduced net primary productivity and temporal variation in community respiration during emersion periods. Our results support the idea that compensatory dynamics may stabilise aggregate properties. They further suggest that the ecological consequences of the loss of a single structurally important species may be stronger than those derived from smaller-scale mechanical disturbances in natural ecosystems

Why Political Science Is an Ethical Issue

To be Complete