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A Simple Reservoir Model of Working Memory with Real Values

Anthony Strock 1 Nicolas Rougier 1 Xavier Hinaut 1
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : The prefrontal cortex is known to be involved in many high-level cognitive functions, in particular, working memory. Here, we study to what extent a group of randomly connected units (namely an Echo State Network, ESN) can store and main-tain (as output) an arbitrary real value from a streamed input, i.e., can act as a sustained working memory unit. Furthermore, we explore to what extent such an architecture can take advantage of the stored value in order to produce non-linear computations. Comparison between different architectures (with and without feedback, with and without a working memory unit) shows that an explicit memory improves the performances.
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Contributor : Nicolas P. Rougier Connect in order to contact the contributor
Submitted on : Friday, June 15, 2018 - 4:16:44 PM
Last modification on : Saturday, December 4, 2021 - 3:05:33 AM
Long-term archiving on: : Monday, September 17, 2018 - 4:04:42 PM


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Anthony Strock, Nicolas Rougier, Xavier Hinaut. A Simple Reservoir Model of Working Memory with Real Values. 2018 International Joint Conference on Neural Networks (IJCNN), Jul 2018, Rio de Janeiro, Brazil. ⟨10.1109/IJCNN.2018.8489262⟩. ⟨hal-01803594⟩



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