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Related Experiment Video

Updated: Jun 7, 2026

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)
09:05

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Published on: June 12, 2017

A neurodynamical model for working memory.

Razvan Pascanu1, Herbert Jaeger

  • 1DIRO, Université de Montréal, H3T 1J4 Quebec, Canada. pascanur@iro.umontreal.ca

Neural Networks : the Official Journal of the International Neural Network Society
|November 2, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a simple working memory model using an echo state network to handle storing, maintaining, retrieving, and deleting information. The model successfully performs bracket parsing on noisy graphical input, demonstrating attractor dynamics.

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Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Existing working memory (WM) models often lack mechanisms for all core functions: storage, maintenance, retrieval, and deletion.
  • A unified neurodynamical approach is needed to integrate these diverse WM operations.

Purpose of the Study:

  • To present a simplified working memory model capable of performing storage, maintenance, retrieval, and deletion.
  • To demonstrate the model's efficacy on a complex cognitive task involving noisy input.
  • To develop a formal framework for describing memory states as attractors in dynamical systems.

Main Methods:

  • Utilized a recurrent neural network (RNN) of the echo state network (ESN) type for the WM model.
  • Trained the ESN model on a bracket level parsing task with rich, noisy graphical script input.
  • Developed a generalized formal framework for attractors in input-driven dynamical systems.

Main Results:

  • The ESN-based WM model successfully executed all required performance modes (storing, maintaining, retrieving, deleting).
  • The model demonstrated robust performance on the bracket parsing task despite noisy and complex input streams.
  • A novel formal framework was proposed to describe memory states as attractors.

Conclusions:

  • A simple, unified neurodynamical model based on ESNs can effectively implement diverse working memory functions.
  • The concept of attractors in input-driven systems provides a valuable dynamical systems perspective on working memory.
  • The proposed formal framework offers a rigorous method for analyzing such memory attractors.