820 resultados para Learning-Related Behavior
Resumo:
Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled. We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path.
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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
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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
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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
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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
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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.
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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.
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Background: One characteristic of post traumatic stress disorder is an inability to adapt to a safe environment i.e. to change behavior when predictions of adverse outcomes are not met. Recent studies have also indicated that PTSD patients have altered pain processing, with hyperactivation of the putamen and insula to aversive stimuli (Geuze et al, 2007). The present study examined neuronal responses to aversive and predicted aversive events. Methods: Twenty-four trauma exposed non-PTSD controls and nineteen subjects with PTSD underwent fMRI imaging during a partial reinforcement fear conditioning paradigm, with a mild electric shock as the unconditioned stimuli (UCS). Three conditions were analyzed: actual presentations of the UCS, events when a UCS was expected, but omitted (CS+), and events when the UCS was neither expected nor delivered (CS-). Results: The UCS evoked significant alterations in the pain matrix consisting of the brainstem, the midbrain, the thalamus, the insula, the anterior and middle cingulate and the contralateral somatosensory cortex. PTSD subjects displayed bilaterally elevated putamen activity to the electric shock, as compared to controls. In trials when USC was expected, but omitted, significant activations were observed in the brainstem, the midbrain, the anterior insula and the anterior cingulate. PTSD subjects displayed similar activations, but also elevated activations in the amygdala and the posterior insula. Conclusions: These results indicate altered fear and safety learning in PTSD, and neuronal activations are further explored in terms of functional connectivity using psychophysiological interaction analyses.
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The orexin/hypocretin (Orx/Hcrt) system has long been considered to regulate a wide range of physiological processes, including feeding, energy metabolism, and arousal. More recently, concordant observations have demonstrated an important role for these peptides in the reinforcing properties of most drugs of abuse. Orx/Hcrt neurons arise in the lateral hypothalamus (LH) and project to all brain structures implicated in the regulation of arousal, stress, and reward. Although Orx/Hcrt neurons have been shown to massively project to the paraventricular nucleus of the thalamus (PVT), only recent evidence suggested that the PVT may be a key relay of Orx/Hcrt-coded reward-related communication between the LH and both the ventral and dorsal striatum. While this thalamic region was not thought to be part of the "drug addiction circuitry," an increasing amount of evidence demonstrated that the PVT-particularly PVT Orx/Hcrt transmission-was implicated in the modulation of reward function in general and several aspects of drug-directed behaviors in particular. The present review discusses recent findings that suggest that maladaptive recruitment of PVT Orx/Hcrt signaling by drugs of abuse may promote persistent compulsive drug-seeking behavior following a period of protracted abstinence and as such may represent a relevant target for understanding the long-term vulnerability to drug relapse after withdrawal.
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Low therapeutic adherence to medication is very common. Clinical effectiveness is related to dose rate and route of administration and so poor therapeutic adherence can reduce the clinical benefit of treatment. The therapeutic adherence of patients with chronic obstructive pulmonary disease (COPD) is extremely poor according to most studies. The research about COPD adherence has mainly focussed on quantifying its effect, and few studies have researched factors that affect non-adherence. Our study will evaluate the effectiveness of a multifactor intervention to improve the therapeutic adherence of COPD patients.
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To further understand the pharmacological properties of N-oleoylethanolamine (OEA), a naturally occurring lipid that activates peroxisome proliferator-activated receptor alpha (PPARα), we designed sulfamoyl analogs based on its structure. Among the compounds tested, N-octadecyl-N'-propylsulfamide (CC7) was selected for functional comparison with OEA. The performed studies include the following computational and biological approaches: 1) molecular docking analyses; 2) molecular biology studies with PPARα; 3) pharmacological studies on feeding behavior and visceral analgesia. For the docking studies, we compared OEA and CC7 data with crystallization data obtained with the reference PPARα agonist GW409544. OEA and CC7 interacted with the ligand-binding domain of PPARα in a similar manner to GW409544. Both compounds produced similar transcriptional activation by in vitro assays, including the GST pull-down assay and reporter gene analysis. In addition, CC7 and OEA induced the mRNA expression of CPT1a in HpeG2 cells through PPARα and the induction was avoided with PPARα-specific siRNA. In vivo studies in rats showed that OEA and CC7 had anorectic and antiobesity activity and induced both lipopenia and decreases in hepatic fat content. However, different effects were observed when measuring visceral pain; OEA produced visceral analgesia whereas CC7 showed no effects. These results suggest that OEA activity on the PPARα receptor (e.g., lipid metabolism and feeding behavior) may be dissociated from other actions at alternative targets (e.g., pain) because other non cannabimimetic ligands that interact with PPARα, such as CC7, do not reproduce the full spectrum of the pharmacological activity of OEA. These results provide new opportunities for the development of specific PPARα-activating drugs focused on sulfamide derivatives with a long alkyl chain for the treatment of metabolic dysfunction.
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The role of lysophosphatidic acid (LPA) in the control of emotional behavior remains to be determined. We analyzed the effects of the central administration of 1-oleoyl-LPA (LPA 18∶1) in rats tested for food consumption and anxiety-like and depression-like behaviors. For this purpose, the elevated plus-maze, open field, Y maze, forced swimming and food intake tests were performed. In addition, c-Fos expression in the dorsal periaqueductal gray matter (DPAG) was also determined. The results revealed that the administration of LPA 18∶1 reduced the time in the open arms of the elevated plus-maze and induced hypolocomotion in the open field, suggesting an anxiogenic-like phenotype. Interestingly, these effects were present following LPA 18∶1 infusion under conditions of novelty but not under habituation conditions. In the forced swimming test, the administration of LPA 18∶1 dose-dependently increased depression-like behavior, as evaluated according to immobility time. LPA treatment induced no effects on feeding. However, the immunohistochemical analysis revealed that LPA 18∶1 increased c-Fos expression in the DPAG. The abundant expression of the LPA1 receptor, one of the main targets for LPA 18∶1, was detected in this brain area, which participates in the control of emotional behavior, using immunocytochemistry. These findings indicate that LPA is a relevant transmitter potentially involved in normal and pathological emotional responses, including anxiety and depression.
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This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified
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The study provides a systematic review that explores the current literature on olfactory capacity in abnormal eating behavior. The objective is to present a basis for discussion on whether research in olfaction in eating disorders may offer additional insight with regard to the complex etiopathology of eating disorders (ED) and abnormal eating behaviors. Electronic databases (Medline, PsycINFO, PubMed, Science Direct, and Web of Science) were searched using the components in relation to olfaction and combining them with the components related to abnormal eating behavior. Out of 1352 articles, titles were first excluded by title (n = 64) and then by abstract and fulltext resulting in a final selection of 14 articles (820 patients and 385 control participants) for this review. The highest number of existing literature on olfaction in ED were carried out with AN patients (78.6%) followed by BN patients (35.7%) and obese individuals (14.3%). Most studies were only conducted on females. The general findings support that olfaction is altered in AN and in obesity and indicates toward there being little to no difference in olfactory capacity between BN patients and the general population. Due to the limited number of studies and heterogeneity this review stresses on the importance of more research on olfaction and abnormal eating behavior.