[Re] How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks

Erwan Le Masson 1 Frédéric Alexandre 1
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : The reference paper introduces a new reinforcement learning model called Attention- Gated MEmory Tagging (AuGMEnT). The results presented suggest new approaches in understanding the acquisition of tasks requiring working memory and attentional feedback, as well as biologically plausible learning mechanisms. The model also improves on previous reinforcement learning schemes by allowing tasks to be expressed more naturally as a sequence of inputs and outputs. A Python implementation of the model is available on the author’s GitHub page which helped to verify the correctness of the computations. The script written for this replication also uses Python along with NumPy.
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The ReScience journal, GitHub, 2016, 2 (1), 〈10.1371/journal.pcbi.1004060〉
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https://hal.inria.fr/hal-01418735
Contributeur : Frédéric Alexandre <>
Soumis le : vendredi 16 décembre 2016 - 21:32:03
Dernière modification le : jeudi 11 janvier 2018 - 06:24:26
Document(s) archivé(s) le : mardi 21 mars 2017 - 09:08:00

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Erwan Le Masson, Frédéric Alexandre. [Re] How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks. The ReScience journal, GitHub, 2016, 2 (1), 〈10.1371/journal.pcbi.1004060〉. 〈hal-01418735〉

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