Emergence of task-dependent representations in working memory circuits

Cristina Savin, Jochen Triesch

Research output: Contribution to journalArticle

Abstract

A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP) and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.

Original languageEnglish (US)
Article number57
JournalFrontiers in Computational Neuroscience
Volume8
Issue numberMAY
DOIs
StatePublished - May 28 2014

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Reward
Short-Term Memory
Learning
Neural Networks (Computer)
Prefrontal Cortex

Keywords

  • Delayed categorization
  • Intrinsic plasticity
  • Prefrontal cortex
  • Reward-dependent learning
  • STDP
  • Synaptic scaling
  • Working memory

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

Cite this

Emergence of task-dependent representations in working memory circuits. / Savin, Cristina; Triesch, Jochen.

In: Frontiers in Computational Neuroscience, Vol. 8, No. MAY, 57, 28.05.2014.

Research output: Contribution to journalArticle

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