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Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition
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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“Biophysically realistic computational models have delineated a neural circuit basis of WM activity through 2 key network properties: Strong recurrent excitation to sustain persistent activity and recruitment of lateral inhibition to shape selectivity of representations (Compte et al. 2000;Brunel and Wang 2001).”
From Linking microcircuit dysfunction to cognitive impairment: Effects of disinhibition associated with schizophrenia in a cortical working memory model (2014) · cites this paper as Brunel and Wang 2001
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Bridging Levels of Understanding in Schizophrenia Through Computational Modeling
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Metadata
- Authors
- Brunel, N., & Wang, X.-J.
- Affiliations
- École Normale Supérieure - PSL (FR)
- Brandeis University (US)
- Venue
- Journal of Computational Neuroscience
- Published
- 2001-07
- Volume
- 11
- Issue
- 1
- Pages
- 63-85
- ISSN
- 0929-5313 (ISSN-L)
- 1573-6873
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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