scholarly journals An integrated homeostatic reinforcement learning theory of motivation explains the transition to cocaine addiction

2015 ◽  
Author(s):  
Mehdi Keramati ◽  
Audrey Durand ◽  
Paul Girardeau ◽  
Boris Gutkin ◽  
Serge Ahmed
Keyword(s):  
Reinforcement Learning ◽  
Learning Theory ◽  
Drugs Of Abuse ◽  
Learning System ◽  
Regulation Mechanism ◽  
Homeostatic Regulation ◽  
Drug Seeking ◽  
Corrective Effect ◽  
Regulation Mechanisms ◽  
Intake Regulation

Drugs of abuse implicate both reward learning and homeostatic regulation mechanisms of the brain. Theories of addiction, thus, have mostly depicted this phenomenon as pathology in either habit-based learning system or homeostatic mechanisms. Showing the limits of those accounts, we hypothesize that compulsive drug seeking arises from drugs hijacking a system that integrates homeostatic regulation mechanism with goal-directed action/behavior. Building upon a recently developed homeostatic reinforcement learning theory, we present a computational theory proposing that cocaine reinforces goal-directed drug-seeking due to its rapid homeostatic corrective effect, whereas its chronic use induces slow and long-lasting changes in homeostatic setpoint. Our theory accounts for key behavioral and neurobiological features of addiction, most notably, escalation of cocaine use, drug-primed craving and relapse, and individual differences underlying susceptibility to addiction. The theory also generates unique predictions about the mechanisms of cocaine-intake regulation and about cocaine-primed craving and relapse that are confirmed by new experiments.

New Supervised Learning Theory Applied to Cerebellar Modeling for Suppression of Variability of Saccade End Points

2013 ◽  
Vol 25 (6) ◽  
pp. 1440-1471 ◽  
Author(s):  
Masahiko Fujita
Keyword(s):  
Supervised Learning ◽  
Cerebellar Cortex ◽  
Learning Theory ◽  
Learning System ◽  
Learning Ability ◽  
Motor Center ◽  
End Points ◽  
Noise Effects ◽  
End Point ◽  
Control Scheme

A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.

scholarly journals Self-Organized Fuzzy Reinforcement Learning System

2003 ◽  
Vol 39 (7) ◽  
pp. 699-701
Author(s):  
Kosuke UMESAKO ◽  
Masanao OBAYASHI ◽  
Kunikazu KOBAYASHI
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Self Organized

FML-Based Reinforcement Learning Agent with Fuzzy Ontology for Human-Robot Cooperative Edutainment

2020 ◽  
Vol 28 (06) ◽  
pp. 1023-1060
Author(s):  
Chang-Shing Lee ◽  
Mei-Hui Wang ◽  
Yi-Lin Tsai ◽  
Wei-Shan Chang ◽  
Marek Reformat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elementary Schools ◽  
Reinforcement Learning ◽  
Bayesian Estimation ◽  
Learning System ◽  
Handheld Devices ◽  
Special Importance ◽  
High Performing ◽  
Physiological Indices ◽  
Learning Agent

The currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that human-robot cooperation is of special importance. Various types of robots have been applied to the so-called field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzy-based system for a human-robot cooperative Edutainment. This co-learning system includes a brain-computer interface (BCI) ontology model and a Fuzzy Markup Language (FML)-based Reinforcement Learning Agent (FRL-Agent). The proposed FRL-Agent is composed of (1) a human learning agent, (2) a robotic teaching agent, (3) a Bayesian estimation agent, (4) a robotic BCI agent, (5) a fuzzy machine learning agent, and (6) a fuzzy BCI ontology. In order to verify the effectiveness of the proposed system, the FRL-Agent is used as a robot teacher in a number of elementary schools, junior high schools, and at a university to allow robot teachers and students to learn together in the classroom. The participated students use handheld devices to indirectly or directly interact with the robot teachers to learn English. Additionally, a number of university students wear a commercial EEG device with eight electrode channels to learn English and listen to music. In the experiments, the robotic BCI agent analyzes the collected signals from the EEG device and transforms them into five physiological indices when the students are learning or listening. The Bayesian estimation agent and fuzzy machine learning agent optimize the parameters of the FRL agent and store them in the fuzzy BCI ontology. The experimental results show that the robot teachers motivate students to learn and stimulate their progress. The fuzzy machine learning agent is able to predict the five physiological indices based on the eight-channel EEG data and the trained model. In addition, we also train the model to predict the other students’ feelings based on the analyzed physiological indices and labeled feelings. The FRL agent is able to provide personalized learning content based on the developed human and robot cooperative edutainment approaches. To our knowledge, the FRL agent has not applied to the teaching fields such as elementary schools before and it opens up a promising new line of research in human and robot co-learning. In the future, we hope the FRL agent will solve such an existing problem in the classroom that the high-performing students feel the learning contents are too simple to motivate their learning or the low-performing students are unable to keep up with the learning progress to choose to give up learning.

Cellular and Molecular Mechanisms of Addiction

2017 ◽  
Author(s):  
Kathryn J. Reissner ◽  
Peter W. Kalivas
Keyword(s):  
Molecular Mechanisms ◽  
Drugs Of Abuse ◽  
Drug Seeking ◽  
Molecular Changes ◽  
Cellular Changes ◽  
Continued Use ◽  
Brain Reward ◽  
Social Use ◽  
The Brain

Exposure to drugs of abuse can be a reinforcing experience that, in vulnerable individuals, can lead to continued use and the development of an addiction disorder. Evidence indicates that the escalation in use and compulsive motivation to obtain the drug is linked to long-lasting cellular changes within the brain reward neurocircuitry. In this chapter we describe the stages of transition in use from social use to habitual relapse, and within that context we describe the implicated neurocircuitry, and the enduring cellular and molecular changes that occur within that circuitry, that may mediate the preoccupation with drug seeking in addiction-vulnerable individuals.

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