We study the short-run effect of involuntary job loss on comprehensive measures of public health costs. We focus on job loss induced by plant closure, thereby addressing the reverse causality problem as job displacements due to plant closure are unlikely caused by workers' health status, but potentially have important effects on individual workers' health and associated public health costs. Our empirical analysis is based on a rich data set from Austria providing comprehensive information on various types of health care costs and day-by-day work history at the individual level. Our central findings are: (i) overall expenditures on medical treatments are not strongly affected by job displacement; (ii) job loss significantly increases expenditures for antidepressants and related drugs, as well as for hospitalizations due to mental health problems for men (but not for women) although the effects are economically rather small; and (iii) sickness benefits strongly increase due to job loss.
Modern bioenergy is seen as a promising option to curb greenhouse gas emissions. There is, however, a potential competition for land and water between bioenergy and food crops. Another question is whether biomass for energy use can be produced in a sustainable manner given the current conventional agricultural production practices. Other than the land and water competition, this question is often neglected in scenarios to meet a significant part of global energy demand with bioenergy. In the following, I address this question. There are sustainable alternatives, for example organic agriculture, to avoid the negative environmental effects of conventional agriculture. Yet, meeting a significant part of global energy demand with biomass grown sustainably may not be possible, as burning significant quantities of organic matter—inherent in bioenergy use—is likely to be incompatible with the principles of such alternatives, which often rely on biomass input for nutrient balance. There may therefore be a trade-off between policies and practices to increase bioenergy and those to increase sustainability in agriculture via practices such as organic farming. This is not a general critique of bioenergy but it points to additional potential dangers of modern bioenergy as a strategy to meet significant parts of world energy demand.
Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways – functional connectivity and effective connectivity. Whereas functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This article reviews the conceptual and methodological bases of established techniques for characterizing functional and effective connectivity using electrophysiological and functional neuroimaging data.
Noninvasive imaging of the brain of animal models demands the detection of increasingly smaller structures by in vivo MRI. The purpose of this work was to elucidate the spatial resolution and structural contrast that can be obtained for studying the brain of C57BL/6J mice by optimized T2-weighted fast spin-echo MRI at 9.4 T. As a prerequisite for high-resolution imaging in vivo, motion artifacts were abolished by combining volatile anesthetics and positive pressure ventilation with a specially designed animal bed for fixation. Multiple substructures in the cortex, olfactory bulb, hippocampus, and cerebellum were resolved at 30 to 40 microm in-plane resolution and 200 to 300 microm section thickness as well as for relatively long echo times of 65 to 82 ms. In particular, the approach resulted in the differentiation of up to five cortical layers. In the olfactory bulb the images unraveled the mitral cell layer which has a thickness of mostly single cells. In the hippocampus at least five substructures could be separated. The molecular layer, Purkinje layer, and granular layer of the cerebellum could be clearly differentiated from the white matter. In conclusion, even without the use of a contrast agent, suitable adjustments of a widely available T2-weighted MRI sequence at high field allow for structural MRI of living mice at near single-cell layer resolution.
In this paper, we describe a generic approach to modelling dynamics in neuronal populations. This approach models a full density on the states of neuronal populations but finesses this high-dimensional problem by re-formulating density dynamics in terms of ordinary differential equations on the sufficient statistics of the densities considered (c.f., the method of moments). The particular form for the population density we adopt is a Gaussian density (c.f., the Laplace assumption). This means population dynamics are described by equations governing the evolution of the population's mean and covariance. We derive these equations from the Fokker-Planck formalism and illustrate their application to a conductance-based model of neuronal exchanges. One interesting aspect of this formulation is that we can uncouple the mean and covariance to furnish a neural-mass model, which rests only on the populations mean. This enables us to compare equivalent mean-field and neural-mass models of the same populations and evaluate, quantitatively, the contribution of population variance to the expected dynamics. The mean-field model presented here will form the basis of a dynamic causal model of observed electromagnetic signals in future work.
In this note, we describe a variant of dynamic causal modelling for evoked responses as measured with electroencephalography or magnetoencephalography (EEG and MEG). We depart from equivalent current dipole formulations of DCM, and extend it to provide spatiotemporal source estimates that are spatially distributed. The spatial model is based upon neural-field equations that model neuronal activity on the cortical manifold. We approximate this description of electrocortical activity with a set of local standing-waves that are coupled though their temporal dynamics. The ensuing distributed DCM models source as a mixture of overlapping patches on the cortical mesh. Time-varying activity in this mixture, caused by activity in other sources and exogenous inputs, is propagated through appropriate lead-field or gain-matrices to generate observed sensor data. This spatial model has three key advantages. First, it is more appropriate than equivalent current dipole models, when real source activity is distributed locally within a cortical area. Second, the spatial degrees of freedom of the model can be specified and therefore optimised using model selection. Finally, the model is linear in the spatial parameters, which finesses model inversion. Here, we describe the distributed spatial model and present a comparative evaluation with conventional equivalent current dipole (ECD) models of auditory processing, as measured with EEG.
This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
