Encoding of Tactile Stimuli by Mechanoreceptors and Interneurons of the Medicinal Leech

For many animals processing of tactile information is a crucial task in behavioral contexts like exploration, foraging, and stimulus avoidance. The leech, having infrequent access to food, developed an energy efficient reaction to tactile stimuli, avoiding unnecessary muscle movements: The local bend behavior moves only a small part of the body wall away from an object touching the skin, while the rest of the animal remains stationary. Amazingly, the precision of this localized behavioral response is similar to the spatial discrimination threshold of the human fingertip, although the leech skin is innervated by an order of magnitude fewer mechanoreceptors and each midbody ganglion contains only 400 individually identified neurons in total. Prior studies suggested that this behavior is controlled by a three-layered feed-forward network, consisting of four mechanoreceptors (P cells), approximately 20 interneurons and 10 individually characterized motor neurons, all of which encode tactile stimulus location by overlapping, symmetrical tuning curves. Additionally, encoding of mechanical force was attributed to three types of mechanoreceptors reacting to distinct intensity ranges: T cells for touch, P cells for pressure, and N cells for strong, noxious skin stimulation. In this study, we provide evidences that tactile stimulus encoding in the leech is more complex than previously thought. Combined electrophysiological, anatomical, and voltage sensitive dye approaches indicate that P and T cells both play a major role in tactile information processing resulting in local bending. Our results indicate that tactile encoding neither relies on distinct force intensity ranges of different cell types, nor location encoding is restricted to spike count tuning. Instead, we propose that P and T cells form a mixed type population, which simultaneously employs temporal response features and spike counts for multiplexed encoding of touch location and force intensity. This hypothesis is supported by our finding that previously identified local bend interneurons receive input from both P and T cells. Some of these interneurons seem to integrate mechanoreceptor inputs, while others appear to use temporal response cues, presumably acting as coincidence detectors. Further voltage sensitive dye studies can test these hypotheses how a tiny nervous system performs highly precise stimulus processing

Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR

In this paper I use the predictive distribution of the back-, now- and forecasts obtained with a mixed-frequency Bayesian VAR (MF-BVAR) to provide a real-time assessment of the probability of a recession in the euro area for the period from 2003 until 2013. Using a dataset that consists of 135 monthly data vintages and covers 11 soft and hard monthly indicators as well as quarterly real GDP, I show that the MF-BVAR is able to capture current economic conditions extremely well. For both recession periods in the sample, the Great Recession of 2008/2009 and the European debt crisis 2011/2013, the MF-BVAR real-time recession probabilities soar right at the onset of the pending slump of GDP growth. By contrast a BVAR estimated on quarterly data detects both recessions with a substantial delay. While typically non-linear discrete-choice or regime switching models have to be used to predict rare events such as recessions, my results indicate that the MF-BVAR can not only compete with other nowcasting approaches in terms of the accuracy of point forecasts, but also reliably detect rare events through the corresponding predictive distribution which is easily available as a by-product of the estimation procedure

Forecasting euro area recessions in real-time

I present evidence that the linear mixed-frequency Bayesian VAR provides very sharp and well calibrated monthly real-time recession probabilities for the euro area for the period from 2004 until 2013. The model outperforms not only the univariate regime-switching models for a number of hard and soft economic indicators and their optimal linear combinations, but also a real-time recession index obtained with Google Trends data. This result holds irrespective of whether the joint predictive distribution of several economic indicators or the marginal distribution of real GDP growth is evaluated to extract the real-time recession probabilities of the mixed-frequency Bayesian VAR. The inclusion of the confidence index in industry turns out to be crucial for the performance of the model

A Theory of Wage Adjustment under Loss Aversion

We present a new theory of wage adjustment, based on worker loss aversion. In line with prospect theory, the workers' perceived utility losses from wage decreases are weighted more heavily than the perceived utility gains from wage increases of equal magnitude. Wage changes are evaluated relative to an endogenous reference wage, which depends on the workers' rational wage expectations from the recent past. By implication, employment responses are more elastic for wage decreases than for wage increases and thus firms face an upward-sloping labor supply curve that is convexly kinked at the workers' reference price. Firms adjust wages flexibly in response to variations in labor demand. The resulting theory of wage adjustment is starkly at variance with past theories. In line with the empirical evidence, we find that (1) wages are completely rigid in response to small labor demand shocks, (2) wages are downward rigid but upward flexible for medium sized labor demand shocks, and (3) wages are relatively downward sluggish for large shocks

A Theory of Wage Adjustment under Loss Aversion

We present a new theory of wage adjustment, based on worker loss aversion. In line with prospect theory, the workers' perceived utility losses from wage decreases are weighted more heavily than the perceived utility gains from wage increases of equal magnitude. Wage changes are evaluated relative to an endogenous reference wage, which depends on the workers' rational wage expectations from the recent past. By implication, employment responses are more elastic for wage decreases than for wage increases and thus firms face an upward-sloping labor supply curve that is convexly kinked at the workers' reference price. Firms adjust wages flexibly in response to variations in labor demand. The resulting theory of wage adjustment is starkly at variance with past theories. In line with the empirical evidence, we find that (1) wages are completely rigid in response to small labor demand shocks, (2) wages are downward rigid but upward flexible for medium sized labor demand shocks, and (3) wages are relatively downward sluggish for large shocks

Forecasting German key macroeconomic variables using large dataset methods

We study the forecasting performance of three alternative large scale approaches using a dataset for Germany that consists of 123 variables in quarterly frequency. These three approaches handle the dimensionality problem evoked by such a large dataset by aggregating information, yet on different levels. We consider different factor models, a large Bayesian vector autoregression and model averaging techniques, where aggregation takes place before, during and after the estimation of the different models, respectively. We find that overall the large Bayesian VAR and the Bayesian factor augmented VAR provide the most precise forecasts for a set of eleven core macroeconomic variables, including GDP growth and CPI inflation, and that the performance of these two models is relatively robust to model misspecification. However, our results also indicate that in many cases the gains in forecasting accuracy relative to a simple univariate autoregression are only moderate and none of the models would have been able to predict the Great Recession

A theory of wage adjustment under loss aversion

We present a new theory of wage adjustment, based on worker loss aversion. In line with prospect theory, the workers' perceived utility losses from wage decreases are weighted more heavily than the perceived utility gains from wage increases of equal magnitude. Wage changes are evaluated relative to an endogenous reference wage, which depends on the workers' rational wage expectations from the recent past. By implication, employment responses are more elastic for wage decreases than for wage increases and thus firms face an upward-sloping labor supply curve that is convexly kinked at the workers' reference wage. Firms adjust wages flexibly in response to variations in labor demand. The resulting theory of wage adjustment is starkly at variance with past theories. In line with the empirical evidence, we find that (1) wages are completely rigid in response to small labor demand shocks, (2) wages are downward rigid but upward flexible for medium sized labor demand shocks, and (3) wages are relatively downward sluggish for large shocks

Forecasting German key macroeconomic variables using large dataset methods

We study the forecasting performance of three alternative large scale approaches for German key macroeconomic variables using a dataset that consists of 123 variables in quarterly frequency. These three approaches handle the dimensionality problem evoked by such a large dataset by aggregating information, yet on different levels. We consider different factor models, a large Bayesian VAR and model averaging techniques, where aggregation takes place before, during and after the estimation of the different models, respectively. We find that overall the large Bayesian VAR provides the most precise forecasts compared to the other large scale approaches and a number of small benchmark models. For some variables the large Bayesian VAR is also the only model producing unbiased forecasts at least for short horizons. While a Bayesian factor augmented VAR with a tight prior also provides quite accurate forecasts overall, the performance of the other methods depends on the variable to be forecast

Coding of touch in neurons of the medicinal leech Hirudo medicinalis

A fundamental question in neuroscience is how neurons code and process sensory information so that precise behavioral responses arise. In this thesis, a small neuronal network of the leech was investigated in order to reveal the neuronal coding strategies of its extremely precise behavior. Several stimulus estimation approaches were used to analyze the neuronal responses of the three mechanosensory cell types and various interneurons, and the results present evidence for multiplexed as well as ensemble encoding of touch stimuli. This thesis provides for the first time a comprehensive picture of coding mechanisms of the three leech mechanosensory cells and insights into the processing of sensory information by interneurons of this network. Moreover, despite the simplicity of the neuronal system, the results suggest fundamental coding strategies in somatosensation