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Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition

Brunel, Nicolas, Wang, Xiao-Jing·2001·Journal of Computational Neuroscience

DOI 10.1023/a:1011204814320

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Abstract

Experimental evidence suggests that the maintenance of an item in working memory is achieved through persistent activity in selective neural assemblies of the cortex. To understand the mechanisms underlying this phenomenon, it is essential to investigate how persistent activity is affected by external inputs or neuromodulation. We have addressed these questions using a recurrent network model of object working memory. Recurrence is dominated by inhibition, although persistent activity is generated through recurrent excitation in small subsets of excitatory neurons. Our main findings are as follows. (1) Because of the strong feedback inhibition, persistent activity shows an inverted U shape as a function of increased external drive to the network. (2) A transient external excitation can switch off a network from a selective persistent state to its spontaneous state. (3) The maintenance of the sample stimulus in working memory is not affected by intervening stimuli (distractors) during the delay period, provided the stimulation intensity is not large. On the other hand, if stimulation intensity is large enough, distractors disrupt sample-related persistent activity, and the network is able to maintain a memory only of the last shown stimulus. (4) A concerted modulation of GABA(A) and NMDA conductances leads to a decrease of spontaneous activity but an increase of persistent activity; the enhanced signal-to-noise ratio is shown to increase the resistance of the network to distractors. (5) Two mechanisms are identified that produce an inverted U shaped dependence of persistent activity on modulation. The present study therefore points to several mechanisms that enhance the signal-to-noise ratio in working memory states. These mechanisms could be implemented in the prefrontal cortex by dopaminergic projections from the midbrain.

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Authors: Brunel, N., & Wang, X.-J.
Affiliations: École Normale Supérieure - PSL (FR)
Brandeis University (US)
ORCID: Brunel, Nicolas
Wang, Xiao-Jing
Venue: Journal of Computational Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2001-07
Volume: 11
Issue: 1
Pages: 63-85
ISSN: 0929-5313 (ISSN-L)
1573-6873
DOI: 10.1023/a:1011204814320
OpenAlex: W4253613142
Cited by: 620
References: 73
Citations by year: 2026: 15 · 2025: 20 · 2024: 25 · 2023: 28 · 2022: 30 · 2021: 35 · 2020: 24 · 2019: 22 · 2018: 17 · 2017: 25 · 2016: 21 · 2015: 28 · 2014: 27 · 2013: 43 · 2012: 28
FWCI: 7.05
Type: journal article
Language: en
Update notices: correction (2014-06-24)
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Journal of Computational Neuroscience (publishedVersion)
Archivio istituzionale della Ricerca - Bocconi (submittedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Keywords: object working memory, persistent activity, recurrent inhibition, cortical network model, neuromodulation, external drive, distractor interference, signal-to-noise ratio, prefrontal cortex, dopaminergic modulation, spontaneous activity

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Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition

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