A biologically inspired neuronal model of reward prediction error computation

Pramod Kaushik 1, 2 Maxime Carrere 2 Frédéric Alexandre 2 Surampudi Raju 1, 3
1 IIIT-H - International Institute of Information Technology, Hyderabad [Hyderabad]
2 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
3 Université d'Hyderabad
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.
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Communication dans un congrès
IJCNN 2017 - International Joint Conference on Neural Networks, May 2017, Anchorage, United States. pp.8, 〈http://www.ijcnn.org/〉
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Pramod Kaushik, Maxime Carrere, Frédéric Alexandre, Surampudi Raju. A biologically inspired neuronal model of reward prediction error computation. IJCNN 2017 - International Joint Conference on Neural Networks, May 2017, Anchorage, United States. pp.8, 〈http://www.ijcnn.org/〉. 〈hal-01528658〉

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