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Robust working memory in a two-dimensional continuous attractor network.

Weronika Wojtak1,2, Stephen Coombes3, Daniele Avitabile4,5

  • 1Research Centre of Mathematics, University of Minho, Guimarães, Portugal.

Cognitive Neurodynamics
|December 23, 2024
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Summary
This summary is machine-generated.

This study introduces a new two-dimensional network model for working memory (WM) that overcomes limitations of standard models. The enhanced model accurately represents WM item quality and avoids biologically unrealistic fine-tuning for neural connections.

Keywords:
Continuous bump attractorMemory fidelityRobust neural integratorTwo-dimensional neural fieldWorking memory

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Area of Science:

  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Continuous Attractor Networks (CANs) are used for working memory (WM) modeling.
  • Standard CANs have limitations in representing representational quality and require fine-tuning.

Purpose of the Study:

  • To develop a novel 2D network model addressing limitations of standard CANs.
  • To investigate representational quality and biological realism in WM models.

Main Methods:

  • Formulated a 2D network model using coupled neural field equations.
  • Analyzed 2D bump attractors for conjunctive WM.
  • Simulated temporal integration of evidence and tested connectivity perturbations.

Main Results:

  • Bump amplitude reflects integrated evidence, improving WM content quality.
  • Network transforms weak memory traces into high-fidelity representations.
  • Model demonstrates robustness against connectivity perturbations, maintaining bump stability.

Conclusions:

  • The proposed 2D CAN model offers a more biologically plausible and accurate representation of working memory.
  • This model advances understanding of neural mechanisms underlying continuous information maintenance in WM.