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A biologically inspired neuronal model of reward prediction error computation
Abstract : The neurocomputational model described here proposes that two dimensions involved in computation of reward prediction errors i.e magnitude and time could be computed separately and later combined unlike traditional reinforcement learning models. The model is built on biological evidences and is able to reproduce various aspects of classical conditioning, namely, the progressive cancellation of the predicted reward, the predictive firing from conditioned stimuli, and delineation of early rewards by showing firing for sooner early rewards and not for early rewards that occur with a longer latency in accordance with biological data.
Cited literature [30 references]
https://hal.inria.fr/hal-01528658
Contributor : Frédéric Alexandre Connect in order to contact the contributor
Submitted on : Monday, May 29, 2017 - 2:16:45 PM
Last modification on : Friday, January 21, 2022 - 3:10:01 AM
Long-term archiving on: : Wednesday, September 6, 2017 - 11:04:52 AM
Contributor : Frédéric Alexandre Connect in order to contact the contributor
Submitted on : Monday, May 29, 2017 - 2:16:45 PM
Last modification on : Friday, January 21, 2022 - 3:10:01 AM
Long-term archiving on: : Wednesday, September 6, 2017 - 11:04:52 AM
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IJCNN2017_RPE_Model_0.6.pdf
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