Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making
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Perceptual learning is reward-based. A recent mathematical analysis showed that any reward-based learning system can learn two tasks only when the mean reward is identical for both tasks [Frémaux, Sprekeler and Gerstner, 2010, The Journal of Neuroscience, ...
Acute stress regulates different aspects of behavioral learning through the action of stress hormones and neuromodulators. Stress effects depend on stressor's type, intensity, timing, and the learning paradigm. In addition, genetic background of animals mi ...
In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this ...
Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types o ...
Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, ...
Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscienc ...
This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement l ...
This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement l ...
Perceptual learning is often considered one of the simplest and basic forms of learning in general. Accordingly, it is usually modeled with simple and basic neural networks which show good results in grasping the empirical data. Simple meets simple. Comple ...
memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural pla ...
