Confidence-controlled hebbian learning efficiently extracts category membership from stimuli encoded in view of a categorization task

In experiments on perceptual decision making, individuals learn a categorization task through trial-and-error protocols. We explore the capacity of a decision-making attractor network to learn a categorization task through reward-based, Hebbian-type modifications of the weights incoming from the sti...

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Bibliographic Details
Main Authors:	Berlemont, K. (Author), Nadal, J.-P (Author)
Format:	Article
Language:	English
Published:	MIT Press Journals 2021
Subjects:	algorithm Algorithms Attractor networks Attractor neural networks Cost functions Decision making Decisions makings Gradient methods Hebbian Hebbian learning human Humans Learn+ learning Learning Learning rates Learning rules nerve cell Neural networks Neural Networks, Computer Neurons Optimization Perceptual decision making physiology reward Reward Trial and error
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Confidence-controlled hebbian learning efficiently extracts category membership from stimuli encoded in view of a categorization task

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