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Fast Hebbian plasticity and working memory.

Anders Lansner1, Florian Fiebig2, Pawel Herman3

  • 1Stockholm University, Department of Mathematics, SE-106 91 Stockholm, Sweden; KTH Royal Institute of Technology, Dept of Computational Science and Technology, 100 44 Stockholm, Sweden; SeRC (Swedish e-Science Research Center), Sweden.

Current Opinion in Neurobiology
|November 19, 2023
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Summary
This summary is machine-generated.

Working memory (WM) models are shifting from persistent activity to activity-silent mechanisms. This review explores fast Hebbian synaptic plasticity as a key driver for these new working memory theories.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience

Background:

  • Working memory (WM) research has long been dominated by the persistent activity hypothesis.
  • Recent experimental evidence highlights limitations of sustained neural activity for short-term information maintenance, particularly for multi-item WM.
  • This has spurred interest in activity-silent WM mechanisms and synaptic plasticity.

Purpose of the Study:

  • To review the evolution of working memory theories and models.
  • To focus on the role of fast Hebbian synaptic plasticity as a potential mechanism for WM.
  • To trace the historical development of WM models based on associative learning.

Main Methods:

  • Literature review of working memory theories and models.
  • Analysis of experimental evidence supporting activity-silent WM.
  • Focus on synaptic plasticity, specifically Hebbian learning.

Main Results:

  • The persistent activity hypothesis, dominant since the mid-1990s, faces challenges.
  • Accumulating evidence supports activity-silent WM mechanisms.
  • Fast synaptic plasticity, particularly Hebbian plasticity, is emerging as a key theoretical direction.

Conclusions:

  • Working memory models are increasingly exploring synaptic plasticity over persistent neural activity.
  • Fast Hebbian plasticity offers a promising framework for understanding short-term information maintenance.
  • Understanding associative learning is crucial for developing new WM theories.