Stress, individual differences, and norepinephrine in reinforcement learning-based prediction of mouse behavior in conditioning and spatial learning
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We revisit a recently developed iterative learning algorithm that enables systems to learn from a repeated operation with the goal of achieving high tracking performance of a given trajectory. The learning scheme is based on a coarse dynamics model of the ...
In (perceptual) learning, performance improves with practice either by changes in sensitivity or decision criterion. Often, changes in sensitivity are regarded as the appropriate measure of learning while changes in criterion are considered unavoidable nui ...
Association for Research in Vision and Ophthalmology2012
Brain-derived neurotrophic factor (BDNF) has been suggested to play a major role in plasticity, neurogenesis and learning in the adult brain. The BDNF gene contains a common val66met polymorphism associated with decreased activity-dependent excretion of BD ...
Many classes of objects can now be successfully detected with statistical machine learning techniques. Faces, cars and pedestrians, have all been detected with low error rates by learning their appearance in a highly generic manner from extensive training ...
Perceptual learning improves with most basic stimuli. Interestingly, performance does not improve when stimuli of two types are randomly presented during training (roving). For example, there is no perceptual learning when left or right bisection stimuli w ...
Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervise ...
While it is well established that stress can modulate declarative learning, very few studies have investigated the influence of stress on non-declarative learning. Here, we studied the influence of post-learning stress, which effectively modulates declarat ...
We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is a relation among those tasks, then the information gained during execution of one task has value for the execution of another task. Cons ...
In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according t ...
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is ...