The Role of Mediodorsal Thalamus in Temporal Differentiation of Reward-Guided Actions

The mediodorsal thalamus (MD) is a crucial component of the neural network involved in the learning and generation of goal-directed actions. A series of experiments reported here examined the contributions of MD to the temporal differentiation of reward-guided actions. In Experiment 1, we trained rats on a discrete-trial, fixed-criterion temporal differentiation task, in which only lever presses exceeding a threshold duration value were rewarded. Pre-training MD lesions impaired temporal differentiation of action duration, by increasing the dispersion of the duration distribution. Post-training MD lesions also impaired differentiation, but by reducing the average emitted press durations, thus shifting the distribution without increasing the dispersion. In Experiment 2, we trained rats to space their lever pressing above criterion inter-press-intervals in order to earn rewards. Both pre-training and post-training MD lesions impaired the differentiation of inter-press-intervals. These results show that MD plays an important role in the acquisition and expression of action differentiation

Business Models in a New Digital Culture: The Open Long Tail Model

New business models are emerging in global markets. Quirky is producing new products designed and developed by the community and finally produced by the 3D printing technology. Google gives his glasses to different developers who build up their own applications. Kickstarter finds the funders by the use of the crowd, paying them back with the future products. Employees, funders, customers and partners do not play a stable role with the organization but revolve around it using different form of collaborations related to the organization’s needs. In this scenario business like Amazon find out their own achievement feeding up different customers’ needs

Endocannabinoid Signaling is Critical for Habit Formation

Extended training can induce a shift in behavioral control from goal-directed actions, which are governed by action-outcome contingencies and sensitive to changes in the expected value of the outcome, to habits which are less dependent on action-outcome relations and insensitive to changes in outcome value. Previous studies in rats have shown that interval schedules of reinforcement favor habit formation while ratio schedules favor goal-directed behavior. However, the molecular mechanisms underlying habit formation are not well understood. Endocannabinoids, which can function as retrograde messengers acting through presynaptic CB1 receptors, are highly expressed in the dorsolateral striatum, a key region involved in habit formation. Using a reversible devaluation paradigm, we confirmed that in mice random interval schedules also favor habit formation compared with random ratio schedules. We also found that training with interval schedules resulted in a preference for exploration of a novel lever, whereas training with ratio schedules resulted in less generalization and more exploitation of the reinforced lever. Furthermore, mice carrying either a heterozygous or a homozygous null mutation of the cannabinoid receptor type I (CB1) showed reduced habit formation and enhanced exploitation. The impaired habit formation in CB1 mutant mice cannot be attributed to chronic developmental or behavioral abnormalities because pharmacological blockade of CB1 receptors specifically during training also impairs habit formation. Taken together our data suggest that endocannabinoid signaling is critical for habit formation

Motivational State and Reward Content Determine Choice Behavior under Risk in Mice

Risk is a ubiquitous feature of the environment for most organisms, who must often choose between a small and certain reward and a larger but less certain reward. To study choice behavior under risk in a genetically well characterized species, we trained mice (C57BL/6) on a discrete trial, concurrent-choice task in which they must choose between two levers. Pressing one lever (safe choice) is always followed by a small reward. Pressing the other lever (risky choice) is followed by a larger reward, but only on some of the trials. The overall payoff is the same on both levers. When mice were not food deprived, they were indifferent to risk, choosing both levers with equal probability regardless of the level of risk. In contrast, following food or water deprivation, mice earning 10% sucrose solution were risk-averse, though the addition of alcohol to the sucrose solution dose-dependently reduced risk aversion, even before the mice became intoxicated. Our results falsify the budget rule in optimal foraging theory often used to explain behavior under risk. Instead, they suggest that the overall demand or desired amount for a particular reward determines risk preference. Changes in motivational state or reward identity affect risk preference by changing demand. Any manipulation that increases the demand for a reward also increases risk aversion, by selectively increasing the frequency of safe choices without affecting frequency of risky choices

Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network

Predicting the solubility of given molecules is an important task in the pharmaceutical industry, and consequently this is a well-studied topic. In this research, we revisited this problem with the advantage of modern computing resources. We applied two machine learning models, a linear regression model and a graph convolutional neural network model, on multiple experimental datasets. Both methods can make reasonable predictions while the GCNN model had the best performance. However, the current GCNN model is a black box, while feature importance analysis from the linear regression model offers more insights into the underlying chemical influences. Using the linear regression model, we show how each functional group affects the overall solubility. Ultimately, knowing how chemical structure influences chemical properties is crucial when designing new drugs. Future work should aim to combine the high performance of GCNNs with the interpretability of linear regression, unlocking new advances in next generation high throughput screening.Comment: 6 pages, 4 figures, 2 table

Instrumental Uncertainty as a Determinant of Behavior Under Interval Schedules of Reinforcement

Interval schedules of reinforcement are known to generate habitual behavior, the performance of which is less sensitive to revaluation of the earned reward and to alterations in the action-outcome contingency. Here we report results from experiments using different types of interval schedules of reinforcement in mice to assess the effect of uncertainty, in the time of reward availability, on habit formation. After limited training, lever pressing under fixed interval (FI, low interval uncertainty) or random interval schedules (RI, higher interval uncertainty) was sensitive to devaluation, but with more extended training, performance of animals trained under RI schedules became more habitual, i.e. no longer sensitive to devaluation, whereas performance of those trained under FI schedules remained goal-directed. When the press-reward contingency was reversed by omitting reward after pressing but presenting reward in the absence of pressing, lever pressing in mice previously trained under FI decreased more rapidly than that of mice trained under RI schedules. Further analysis revealed that action-reward contiguity is significantly reduced in lever pressing under RI schedules, whereas action-reward correlation is similar for the different schedules. Thus the extent of goal-directedness could vary as a function of uncertainty about the time of reward availability. We hypothesize that the reduced action-reward contiguity found in behavior generated under high uncertainty is responsible for habit formation

Stability of stationary solutions for nonintegrable peakon equations

The Camassa-Holm equation with linear dispersion was originally derived as an asymptotic equation in shallow water wave theory. Among its many interesting mathematical properties, which include complete integrability, perhaps the most striking is the fact that in the case where linear dispersion is absent it admits weak multi-soliton solutions - "peakons" - with a peaked shape corresponding to a discontinuous first derivative. There is a one-parameter family of generalized Camassa-Holm equations, most of which are not integrable, but which all admit peakon solutions. Numerical studies reported by Holm and Staley indicate changes in the stability of these and other solutions as the parameter varies through the family. In this article, we describe analytical results on one of these bifurcation phenomena, showing that in a suitable parameter range there are stationary solutions - "leftons" - which are orbitally stable