AIMC Topic: Reward

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Model-based reinforcement learning with dimension reduction.

Neural networks : the official journal of the International Neural Network Society Aug 24, 2016
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derive...
Least-Squares Analysis Reward Entropy Reinforcement, Psychology Artificial Intelligence Machine Learning Learning Models, Theoretical
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Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity.

Frontiers in neural circuits Jul 21, 2016
The brain enables animals to behaviorally adapt in order to survive in a complex and dynamic environment, but how reward-oriented behaviors are achieved and computed by its underlying neural circuitry is an open question. To address this concern, we ...
Parkinson Disease Reward Neural Pathways Reinforcement, Psychology Basal Ganglia Humans Neural Networks, Computer Neuronal Plasticity
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Non-reward neural mechanisms in the orbitofrontal cortex.

Cortex; a journal devoted to the study of the nervous system and behavior Jul 15, 2016
Single neurons in the primate orbitofrontal cortex respond when an expected reward is not obtained, and behaviour must change. The human lateral orbitofrontal cortex is activated when non-reward, or loss occurs. The neuronal computation of this negat...
Action Potentials Neurons Reward Nerve Net Humans Neural Networks, Computer Computer Simulation Prefrontal Cortex Emotions
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A neural model of the frontal eye fields with reward-based learning.

Neural networks : the official journal of the International Neural Network Society May 24, 2016
Decision-making is a flexible process dependent on the accumulation of various kinds of information; however, the corresponding neural mechanisms are far from clear. We extended a layered model of the frontal eye field to a learning-based model, usin...
Reward Choice Behavior Psychomotor Performance Animals Orientation Humans Decision Making Visual Fields Saccades Neural Networks, Computer Reaction Time Frontal Lobe Machine Learning Neurons
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A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning.

International journal of neural systems Apr 28, 2016
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shap...
Decision Making Robotics Neurons Reward Drosophila melanogaster Perception Motor Activity Mushroom Bodies Learning Animals Models, Neurological Action Potentials Neural Networks, Computer Computer Simulation
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Demixed principal component analysis of neural population data.

eLife Apr 12, 2016
Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information...
Decision Making Principal Component Analysis Memory, Short-Term Reward Animals Prefrontal Cortex Rats Macaca mulatta Spatial Navigation Sensory Receptor Cells Motor Neurons Datasets as Topic Task Performance and Analysis Olfactory Perception Multifactor Dimensionality Reduction
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Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity.

PloS one Jan 25, 2016
At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. H...
Humans Neural Networks, Computer Machine Learning Laryngeal Muscles Nerve Net Phonetics Motor Cortex Language Development Computer Simulation Auditory Perception Neuronal Plasticity Feedback Motor Neurons Models, Neurological Lip Infant Reward Jaw Dopamine
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Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.

PLoS computational biology Sep 25, 2015
Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on rewa...
Learning Humans Models, Neurological Animals Models, Statistical Machine Learning Male Reward Algorithms Computational Biology Macaca mulatta
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Watching from a distance: A robotically controlled laser and real-time subject tracking software for the study of conditioned predator/prey-like interactions.

Journal of neuroscience methods Jun 22, 2015
BACKGROUND: The physical distance between predator and prey is a primary determinant of behavior, yet few paradigms exist to study this reliably in rodents.
Avoidance Learning Rats, Inbred F344 Software Predatory Behavior Rats, Long-Evans Animals Models, Biological Male Reward Time Factors Lasers Rats Brain-Computer Interfaces Robotics
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A biological mechanism for Bayesian feature selection: Weight decay and raising the LASSO.

Neural networks : the official journal of the International Neural Network Society Mar 25, 2015
Biological systems are capable of learning that certain stimuli are valuable while ignoring the many that are not, and thus perform feature selection. In machine learning, one effective feature selection approach is the least absolute shrinkage and s...
Normal Distribution Models, Neurological Online Systems Humans Bayes Theorem Machine Learning Reward Algorithms Computer Simulation
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