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Biological Cybernetics

, Volume 96, Issue 4, pp 407–419 | Cite as

Neuronal firing rates account for distractor effects on mnemonic accuracy in a visuo-spatial working memory task

  • Julian Macoveanu
  • Torkel Klingberg
  • Jesper TegnérEmail author
Original Paper

Abstract

Persistent neural activity constitutes one neuronal correlate of working memory, the ability to hold and manipulate information across time, a prerequisite for cognition. Yet, the underlying neuronal mechanisms are still elusive. Here, we design a visuo- spatial delayed-response task to identify the relationship between the cue-distractor spatial distance and mnemonic accuracy. Using a shared experimental and computational test protocol, we probe human subjects in computer experiments, and subsequently we evaluate different neural mechanisms underlying persistent activity using an in silico prefrontal network model. Five modes of action of the network were tested: weak or strong synaptic interactions, wide synaptic arborization, cellular bistability and reduced synaptic NMDA component. The five neural mechanisms and the human behavioral data, all exhibited a significant deterioration of the mnemonic accuracy with decreased spatial distance between the distractor and the cue. A subsequent computational analysis revealed that the firing rate and not the neural mechanism per se, accounted for the positive correlation between mnemonic accuracy and spatial distance. Moreover, the computational modeling predicts an inverse correlation between accuracy and distractibility. In conclusion, any pharmacological modulation, pathological condition or memory training paradigm targeting the underlying neural circuitry and altering the net population firing rate during the delay is predicted to determine the amount of influence of a visual distraction.

Keywords

NMDA Work Memory Capacity Persistent Activity Work Memory Training Bistable Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Julian Macoveanu
    • 1
  • Torkel Klingberg
    • 2
  • Jesper Tegnér
    • 1
    • 3
    • 4
    Email author
  1. 1.Computational Biology, Department of PhysicsLinköping University of TechnologyLinköpingSweden
  2. 2.Department of Woman and Child Health, MR centerKarolinska InstitutetStockholmSweden
  3. 3.Computational Medicine Group, Department of MedicineKarolinska InstitutetStockholmSweden
  4. 4.Division for Computational Biology, Department of Physics, IFMLinköping University of TechnologyLinköpingSweden

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