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Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
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A Robust Model of Gated Working Memory.

Anthony Strock1, Xavier Hinaut2, Nicolas P Rougier3

  • 1Inria Bordeaux Sud-Ouest, 33405 Talence Cedex, France; LaBRI, Université de Bordeaux, Institut Polytechnique de Bordeaux, Centre National de la Recherche Scientifique, 33405 Talence Cedex, France; and Institut des Maladies Neurodégénératives, Université de Bordeaux, Centre National de la Recherche Scientifique, 33076 Cedex, Bordeaux, France Anthony.Strock@inria.fr.

Neural Computation
|November 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a simple, random recurrent neural network model for gated working memory. It demonstrates that robust working memory can emerge implicitly from random neural populations through learning.

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Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Gated working memory (GWM) is crucial for cognitive tasks, traditionally modeled with explicit mechanisms.
  • Electrophysiological data has guided computational models of GWM, often relying on dedicated neural components.

Discussion:

  • The proposed model utilizes an implicit mechanism within a random recurrent neural network (RNN) for GWM.
  • It introduces a simple yet robust reservoir model capable of instantaneous updates and storing arbitrary real values over extended periods.

Key Insights:

  • The model's dynamics reveal a line attractor that learns to leverage reentry and nonlinearity during training.
  • Robust GWM emerges across a wide range of hyperparameters, suggesting it's an implicit property of large, mixed neural populations.

Outlook:

  • This work proposes that GWM can be an emergent, implicit property acquired through learning in random neural networks.
  • The model offers an explanation for counterintuitive electrophysiological recordings by treating GWM as a physically open yet functionally closed system.