Prévalence des troubles de l'humeur dans la maladie de Parkinson traitée par stimulation cérébrale profonde [Prevalence of mood disorders in Parkinson's disease patients treated with subthalamic nucleus deep brain stimulation].

Subthalamic nucleus deep brain stimulation (STN-DBS) is a recognized treatment for advanced and severe forms of Parkinson's Disease. The procedure improves motor signs and often allows a reduction of the medication. The impact of the procedure on cognitive and neuropsychiatric signs of the disease is more debated and there is an international consensus for the need of a multidisciplinary evaluation of patients undergoing such programs, including a neuropsychiatric assessment. We present a review of the literature as well as the experience at our centre focused on the short and long term outcome on mood following STN-DBS.

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

Visuo-motor and cognitive procedural learning in children with basal ganglia pathology.

We investigated procedural learning in 18 children with basal ganglia (BG) lesions or dysfunctions of various aetiologies, using a visuo-motor learning test, the Serial Reaction Time (SRT) task, and a cognitive learning test, the Probabilistic Classification Learning (PCL) task. We compared patients with early (<1 year old, n=9), later onset (>6 years old, n=7) or progressive disorder (idiopathic dystonia, n=2). All patients showed deficits in both visuo-motor and cognitive domains, except those with idiopathic dystonia, who displayed preserved classification learning skills. Impairments seem to be independent from the age of onset of pathology. As far as we know, this study is the first to investigate motor and cognitive procedural learning in children with BG damage. Procedural impairments were documented whatever the aetiology of the BG damage/dysfunction and time of pathology onset, thus supporting the claim of very early skill learning development and lack of plasticity in case of damage.

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasinglypopular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigationaladaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Confabulation in schizophrenia: a neuropsychological study.

Confabulation has been documented in schizophrenia, but its neuropsychological correlates appear to be different from those of confabulation in neurological disease states. Forty-five schizophrenic patients and 37 controls were administered a task requiring them to recall fables. They also underwent testing with a range of memory and executive tasks. The patients with schizophrenia produced significantly more confabulations than the controls. After correcting for multiple comparisons, confabulation was not significantly associated with memory impairment, and was associated with impairment on only one of eight executive measures, the Brixton Test. Confabulation scores were also associated with impairment on two semantic memory tests. Confabulation was correlated with intrusion errors in recall, but not false positive errors in a recognition task. The findings suggest that confabulation in schizophrenia is unrelated to the episodic memory impairment seen in the disorder. However, the association with a circumscribed deficit in executive function could be consistent with a defective strategic retrieval account of confabulation similar to that of Moscovitch and co-workers, interacting with defective semantic memory.

Differential cognitive impairment for diverse forms of multiple sclerosis.

BACKGROUND Cognitive impairment is a common feature in multiple sclerosis (MS) patients and occurs in 60% of all cases. Unfortunately, neurological examination does not always agree with the neuropsychological evaluation in determining the cognitive profile of the patient. On the other hand, psychophysiological techniques such as event-related potentials (ERPs) can help in evaluating cognitive impairment in different pathologies. Behavioural responses and EEG signals were recorded during the experiment in three experimental groups: 1) a relapsing-remitting group (RRMS), 2) a benign multiple sclerosis group (BMS) and 3) a Control group. The paradigm employed was a spatial attention task with central cues (Posner experiment). The main aim was to observe the differences in the performance (behavioural variables) and in the latency and amplitude of the ERP components among these groups. RESULTS Our data indicate that both MS groups showed poorer task performance (longer reaction times and lower percentage of correct responses), a latency delay for the N1 and P300 component, and a different amplitude for the frontal N1. Moreover, the deficit in the BMS group, indexed by behavioural and pyschophysiological variables, was more pronounced compared to the RRMS group. CONCLUSION The present results suggest a cognitive impairment in the information processing in all of these patients. Comparing both pathological groups, cognitive impairment was more accentuated in the BMS group compared to the RMSS group. This suggests a silent deterioration of cognitive skills for the BMS that is not usually treated with pharmacological or neuropsychological therapy.

A teaching/learning support tool for introductory programming courses

In This work we present a Web-based tool developed with the aim of reinforcing teaching and learning of introductory programming courses. This tool provides support for teaching and learning. From the teacher's perspective the system introduces important gains with respect to the classical teaching methodology. It reinforces lecture and laboratory sessions, makes it possible to give personalized attention to the student, assesses the degree of participation of the students and most importantly, performs a continuous assessment of the student's progress. From the student's perspective it provides a learning framework, consisting in a help environment and a correction environment, which facilitates their personal work. With this tool students are more motivated to do programming

PsyCoLaUS: santé mentale et maladies cardiovasculaires: association fortuite [PsyCoLaus: mental disorders and cardiovascular diseases: spurious association?].

Cardio-vascular diseases (CVD), their well established risk factors (CVRF) and mental disorders are common and co-occur more frequently than would be expected by chance. However, the mechanisms underlying this association are still poorly understood. The main study questions of PsyCoLaus, the psychiatric arm of CoLaus, are: 1) Do mental disorders increase vulnerability to CVRF and CVD? 2) Do CVRF and CVD promote the development of mental disorders? 3) Do CVRF/ CVD and mental disorders share common pathogenetic processes? The longitudinal project adds a comprehensive psychiatric evaluation to the CoLaus investigation. A better understanding of the psychological, physiological and behavioral links underlying CVD/ CVRF and mental disorders will result in the development of more specific and efficient strategies of prevention and treatment for both psychiatric and CVD/CVRF, two major elements of burden of disease.

Endocannabinoid regulation of acute and protracted nicotine withdrawal: effect of FAAH inhibition.

Evidence shows that the endocannabinoid system modulates the addictive properties of nicotine. In the present study, we hypothesized that spontaneous withdrawal resulting from removal of chronically implanted transdermal nicotine patches is regulated by the endocannabinoid system. A 7-day nicotine dependence procedure (5.2 mg/rat/day) elicited occurrence of reliable nicotine abstinence symptoms in Wistar rats. Somatic and affective withdrawal signs were observed at 16 and 34 hours following removal of nicotine patches, respectively. Further behavioral manifestations including decrease in locomotor activity and increased weight gain also occurred during withdrawal. Expression of spontaneous nicotine withdrawal was accompanied by fluctuation in levels of the endocannabinoid anandamide (AEA) in several brain structures including the amygdala, the hippocampus, the hypothalamus and the prefrontal cortex. Conversely, levels of 2-arachidonoyl-sn-glycerol were not significantly altered. Pharmacological inhibition of fatty acid amide hydrolase (FAAH), the enzyme responsible for the intracellular degradation of AEA, by URB597 (0.1 and 0.3 mg/kg, i.p.), reduced withdrawal-induced anxiety as assessed by the elevated plus maze test and the shock-probe defensive burying paradigm, but did not prevent the occurrence of somatic signs. Together, the results indicate that pharmacological strategies aimed at enhancing endocannabinoid signaling may offer therapeutic advantages to treat the negative affective state produced by nicotine withdrawal, which is critical for the maintenance of tobacco use.