Development of behavior and learning inAplysia

Summary A set of fundamental issues in neuroethology concerns the neural mechanisms underlying behavior and behavioral plasticity. We have recently analyzed these issues by combining a simple systems approach in the marine molluscAplysia with a developmental analysis aimed at examining the emergence and maturation of different forms of behavior and learning. We have focussed on two kinds of questions: 1) How are specific neural circuits developmentally assembled to mediate different types of behaviors? and 2) how is plasticity integrated with these circuits to give rise to different forms of learning? From our analysis of the development of learning and memory inAplysia, several themes have emerged: 1) Different forms of learning emerge according to different developmental timetables. 2) Cellular analogs of learning have the same developmental timetables as their respective forms of behavioral learing. 3) An analysis of non-decremented responses prior to the emergence of sensitization reveals a novel inhibitory process on both behavioral and cellular levels. 4) Sensitization emerges simultaneously in diverse response systems, suggesting an underlying general process. 5) A widespread proliferation of central neurons occurs in the same developmental stage as the emergence of sensitization, raising the possibility that some aspect of the trigger for neuronal proliferation may also contribute to the expression of sensitization.     

Neural mechanisms of operant conditioning and learning-induced behavioral plasticity in <Emphasis Type="Italic">Aplysia</Emphasis>

Associative learning in goal-directed behaviors, in contrast to reflexive behaviors, can alter processes of decision-making in the selection of appropriate action and its initiation, thereby enabling animals, including humans, to gain a predictive understanding of their external environment. In the mollusc Aplysia, recent studies on appetitive operant conditioning in which the animal learns about the positive consequences of its behavior have provided insights into this form of associative learning which, although ubiquitous, remains mechanistically poorly understood. The findings support increasing evidence that central circuit- and cell-wide sites other than chemical synaptic connections, including electrical coupling and membrane conductances controlling intrinsic neuronal excitability and underlying voltage-dependent plateauing or oscillatory mechanisms, may serve as the neural substrates for behavioral plasticity resulting from operant conditioning. Aplysia therefore continues to provide a model system for understanding learning and memory formation that enables establishing the neurobiological links between behavioral, network, and cellular levels of analysis.     

Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models

Agricultural productivity highly depends on the cost of energy required for cultivation. Thus prior knowledge of energy consumption is an important step for energy planning and policy development in agriculture. The aim of the present study is to evaluate the application potential of multiple linear regression (MLR) and machine learning tools such as support vector regression (SVR) and Gaussian process regression (GPR) to forecast the agricultural energy consumption of Turkey. In the development of the models, widespread indicators such as agricultural value-added, total arable land, gross domestic product share of agriculture, and population data were used as input parameters. Twenty-eight-year historical data from 1990 to 2017 were utilized for the training and testing stages of the models. A Bayesian optimization method was applied to improve the prediction capability of SVR and GPR models. The performance of the models was measured by various statistical tools. The results indicated that the Bayesian optimized GPR (BGPR) model with exponential kernel function showed a superior prediction capability over MLR and Bayesian optimized SVR model. The root mean square error, mean absolute deviation, mean absolute percentage error, and coefficient of determination (R²) values for the BGPR model were determined as 0.0022, 0.0005, 0.2041, and 0.9999 in the training phase and 0.0452, 0.0310, 7.7152, and 0.9677 in the testing phase, respectively. As a result, it can be concluded that the proposed BGPR model is an efficient technique and has the potential to predict agricultural energy consumption with high accuracy.     

Judgement in learning‐curve forecasting: a laboratory study

This study investigates whether human judgement can be of value to users of industrial learning curves, either alone or in conjunction with statistical models. In a laboratory setting, it compares the forecast accuracy of a statistical model and judgemental forecasts, contingent on three factors: the amount of data available prior to forecasting, the forecasting horizon, and the availability of a decision aid (projections from a fitted learning curve). The results indicate that human judgement was better than the curve forecasts overall. Despite their lack of field experience with learning curve use, 52 of the 79 subjects outperformed the curve on the set of 120 forecasts, based on mean absolute percentage error. Human performance was statistically superior to the model when few data points were available and when forecasting further into the future. These results indicate substantial potential for human judgement to improve predictive accuracy in the industrial learning‐curve context. Copyright © 1999 John Wiley & Sons, Ltd.     

Stock index prediction based on wavelet transform and FCD-MLGRU

With the development of artificial intelligence, deep learning is widely used in the field of nonlinear time series forecasting. It is proved in practice that deep learning models have higher forecasting accuracy compared with traditional linear econometric models and machine learning models. With the purpose of further improving forecasting accuracy of financial time series, we propose the WT-FCD-MLGRU model, which is the combination of wavelet transform, filter cycle decomposition and multilag neural networks. Four major stock indices are chosen to test the forecasting performance among traditional econometric model, machine learning model and deep learning models. According to the result of empirical analysis, deep learning models perform better than traditional econometric model such as autoregressive integrated moving average and improved machine learning model SVR. Besides, our proposed model has the minimum forecasting error in stock index prediction.     

Memory

Our understanding of the cellular and molecular mechanisms underlying learning and memory formation derives from studies of species as diverse as worms, mollusks, insects, birds and mammals. Despite the quite different brain structures and neuronal networks, the studies support the current notion that neuronal activity leads to changes in synaptic connections as the neural substrate of behavioral plasticity. The analysis of the mechanisms underlying learning and memory formation reveals a surprisingly high conservation between invertebrates and mammals, both at the behavioral as well as the molecular level. This special issue provides an overview of the current knowledge on cellular and molecular processes underlying memory formation. The contributing reviews summarize the findings in different organisms, such as Aplysia, Drosophila, honeybees and mammals, and discuss new approaches, developments and hypotheses all aimed at understanding how the nervous system acquires, stores and retrieves information.     

Sources of error in forecasts and expectations: U.K. economic models, 1984–8

This paper presents a comparative analysis of the sources of error in forecasts for the UK economy published over a recent four-year period by four independent groups. This analysis rests on the archiving at the ESRC Macroeconomic Modelling Bureau of the original forecasts together with all their accompanying assumptions and adjustments. A method of decomposing observed forecast errors so as to distinguish the contributions of forecaster and model is set out; the impact of future expectations treated in a ‘model-consistent’ or ‘rational’ manner is specifically considered. The results show that the forecaster's adjustments make a substantial contribution to forecast performance, a good part of which comes from adjustments that bring the model on track at the start of the forecast period. The published ex-ante forecasts are usually superior to pure model-based ex-post forecasts, whose performance indicates some misspecification of the underlying models.     

A Time‐Simultaneous Prediction Box for a Multivariate Time Series

A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L_∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd.     

Host trail following by the guest antFormicoxenus provancheri

Formicoxenus provancheri, a guest ant ofMyrmica incompleta, is able to follow artificial trails made with the poison gland secretion of its host. The trail-following response is elicited at the same range of concentrations as for the host species. The performance ofFormicoxenus is enhanced by the presence of the host. The adaptive value of these phenomena is discussed.     

Backtesting Value‐at‐Risk: A Generalized Markov Test

Testing the validity of value‐at‐risk (VaR) forecasts, or backtesting, is an integral part of modern market risk management and regulation. This is often done by applying independence and coverage tests developed by Christoffersen (International Economic Review, 1998; 39(4), 841–862) to so‐called hit‐sequences derived from VaR forecasts and realized losses. However, as pointed out in the literature, these aforementioned tests suffer from low rejection frequencies, or (empirical) power when applied to hit‐sequences derived from simulations matching empirical stylized characteristics of return data. One key observation of the studies is that higher‐order dependence in the hit‐sequences may cause the observed lower power performance. We propose to generalize the backtest framework for VaR forecasts, by extending the original first‐order dependence of Christoffersen to allow for a higher‐ or kth‐order dependence. We provide closed‐form expressions for the tests as well as asymptotic theory. Not only do the generalized tests have power against kth‐order dependence by definition, but also included simulations indicate improved power performance when replicating the aforementioned studies. Further, included simulations show much improved size properties of one of the suggested tests. Copyright © 2017 John Wiley & Sons, Ltd.     

卡通—纹理字典学习的图像分解方法

结合变分方法与字典学习,提出一个新的图像分解变分模型和由扩散流引导的字典学习算法.实验结果表明,和已有图像分解方法相比,新算法自适应性强,不仅可以更好地将图像的卡通和纹理分开,而且能有效地将纹理中的噪声去除.     

The effects of caffeine on physiological functions and mental performance

Summary Polygraphic monitoring of several physiological variables was done throughout an experiment investigating the effects of caffeine on mental performance. The experiment started with a mental maze learning task. Then the subjects were given the test beverages according to the group design (CC group (N=16)300 mg caffeine in decaffeinated coffee, DC group (N=16): decaffeinated coffee, WW group (N=8): warm water, and NB group (N=8): no beverage). The experiment continued with a letter cancellation task which was followed by a second mental maze learning task. The caffeine treated subjects differed from the other groups by increased regularity of letter cancellation performance, as indicated by decreases in intraindividual variance. They also differed from the other groups by a slight but significant acrodermal vasoconstriction. No intergroup differences were obtained for mental maze learning, heart rate, respiration, muscle tension, and skin conductance. The results suggest therefore that the drug at this dose level improves behavioral routine and speed rather than cognitive functions and that the vegetative side effects are minimal.     

Merging monthly and quarterly forecasts: Experience with mqem

Forecasts from quarterly econometric models are typically revised on a monthly basis to reflect the information in current economic data. The revision process usually involves setting targets for the quarterly values of endogenous variables for which monthly observations are available and then altering the intercept terms in the quarterly forecasting model to achieve the target values. A formal statistical approach to the use of monthly data to update quarterly forecasts is described and the procedure is applied to the Michigan Quarterly Econometric Model of the US Economy. The procedure is evaluated in terms of both ex post and ex ante forecasting performance. The ex ante results for 1986 and 1987 indicate that the method is quite promising. With a few notable exceptions, the formal procedure produces forecasts of GNP growth that are very close to the published ex ante forecasts.     

Re-examining the impact of European astronomy in seventeenth-century China: a study of Xue Fengzuo’s system of thought and his integration of Chinese and Western knowledge

ABSTRACT

During the late Ming and early Qing period, Jesuit missionaries introduced European science into China, and thereby profoundly influenced the later development of Chinese astronomy. Not only did European astronomy become the official system of the Qing dynasty, but the traditional way to ‘attain up above’ by connecting the study of astronomy and Yi learning gradually fell into disuse. However, the astronomers in this period expressed different views on these two processes. As one of the most important early Qing astronomers, Xue Fengzuo’s case presents a distinctive and important example. Firstly, under the influences of both Chinese tradition and European science, Xue Fengzuo rebuilt the way to ‘attain up above’ based on his three-fold ‘calendrical learning’, i.e. calendrical astronomy, astrology and related pragmatic applications, through which he could realize the highest Confucian ideal. Secondly, he integrated Chinese and Western knowledge for all three aspects of his ‘calendrical learning’, instead of ceding the dominant position to Western methods. From Xue Fengzuo’s example, many of the complex effects of the encounter between different cultures and the process of knowledge transfer can be revealed.     

Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning

PM_2.5 mass concentration prediction is an important research issue because of the increasing impact of air pollution on the urban environment. In this paper, a PM_2.5 forecasting framework incorporating meteorological factors based on multiple kernel learning (MKL) is proposed to forecast the near future PM_2.5. In addition, we develop a novel two-step algorithm for solving the primal MKL problem. Compared with most existing MKL 2-step algorithms, the proposed algorithm does not require the optimal step size for updating kernel combination coefficients by linear search. To demonstrate the performance of the proposed forecasting framework, its performance is compared to single kernel-based support vector regression (SVR). Data sets of an inland city Beijing acquired from UCI are used to train and validate both of two methods. Experiments show that our proposed method outperforms the SVR.     

Intrinsic bubbles revisited: evidence from nonlinear cointegration and forecasting

This paper offers strong further empirical evidence to support the intrinsic bubble model of stock prices, developed by Froot and Obstfeld (American Economic Review, 1991), in two ways. First, our results suggest that there is a long‐run nonlinear relationship between stock prices and dividends for the US stock market during the period 1871–1996. Second, we find that the out‐of‐sample forecasting performance of the intrinsic bubbles model is significantly better than the performance of two alternatives, namely the random walk and the rational bubbles model. Copyright © 2004 John Wiley & Sons, Ltd.     

Short‐term salmon price forecasting

This study establishes a benchmark for short‐term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k‐nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naïve benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.     

Eine Dressurmethode für wahrnehmungsphysiologische Untersuchungen und ihre Anwendung in Orientierungsversuchen mit Vögeln

Summary A conditioning method for the investigation of bird orientation is described: A duck (Anas platyrhynchos) is attached to a slowly rotating turntable, and its heart rate is recorded (Figure 1). When facing a certain direction, the animal gets a weak electrical shock. If the bird is able to determine this direction, its heart rate increases in anticipation of the shock, and there is a maximum at this angle even if no shock at all is applied (Figure 2). By this means it is possible to show whether a certain stimulus situation is appropriate to establish a conditioned reaction of this kind (Figure 3). It is assumed that this method can easily be adapted to investigate other problems of perception and learning.

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Mit Unterstützung der Stiftung Volkswagenwerk.     

PARX model for football match predictions

We propose an innovative approach to model and predict the outcome of football matches based on the Poisson autoregression with exogenous covariates (PARX) model recently proposed by Agosto, Cavaliere, Kristensen, and Rahbek (Journal of Empirical Finance, 2016, 38(B), 640–663). We show that this methodology is particularly suited to model the goal distribution of a football team and provides a good forecast performance that can be exploited to develop a profitable betting strategy. This paper improves the strand of literature on Poisson‐based models, by proposing a specification able to capture the main characteristics of goal distribution. The betting strategy is based on the idea that the odds proposed by the market do not reflect the true probability of the match because they may also incorporate the betting volumes or strategic price settings in order to exploit betters' biases. The out‐of‐sample performance of the PARX model is better than the reference approach by Dixon and Coles (Applied Statistics, 1997, 46(2), 265–280). We also evaluate our approach in a simple betting strategy, which is applied to English football Premier League data for the 2013–2014, 2014–2015, and 2015–2016 seasons. The results show that the return from the betting strategy is larger than 30% in most of the cases considered and may even exceed 100% if we consider an alternative strategy based on a predetermined threshold, which makes it possible to exploit the inefficiency of the betting market.     

The role of Ca2+/calmodulin-stimulable adenylyl cyclases as molecular coincidence detectors in memory formation

Evidence from systems as diverse as mollusks, insects and mammals has revealed that adenylyl cyclase, cyclic adenosine 3′,5′-monophosphate (cAMP) cascade, cAMP-dependent protein kinases and their substrates are required for the cellular events underlying the short-term and long-term forms of memory. In Aplysia and Drosophila models, the coincident activation of independent paths converge to produce a synergistic activation of Ca²⁺/calmodulin-stimulable adenylyl cyclase, thereby enhancing the cAMP level that appears as the primary mediator of downstream events that strengthen enduring memory. In mammals, in which long-term memories require hippocampal function, our understanding of the role of adenylyl cyclases is still fragmentary. Of the differently regulated isoforms present in the hippocampus, the susceptibility of type 1 and type 8 to stimulation by the complex Ca²⁺/calmodulin and their expression in the hippocampus suggest a role for these two isoforms as a molecular coincidence device for hippocampus-related memory function. Here, we review the key features of Ca²⁺/calmodulin stimulable adenylyl cyclases, as well as the involvement of cAMP-regulated signaling pathway in the processes of learning and memory.