Cited by 620Neural dynamics and brain functionMemory and Neural MechanismsNeuroscience and Neuropharmacology Research

The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.

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

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1023/a:1011204814320

Cites

73 of 73 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex
Funahashi, S., Bruce, C. J., & Goldman-Rakic, P. S. (1989) · Journal of Neurophysiology · doi:10.1152/jn.1989.61.2.331 · Cited by 2,685
Cellular basis of working memory
Goldman-Rakic, P. S. (1995) · Neuron · doi:10.1016/0896-6273(95)90304-6 · Cited by 2,663
Neuron Activity Related to Short-Term Memory
Fuster, J. M., & Alexander, G. E. (1971) · Science · doi:10.1126/science.173.3997.652 · Cited by 2,085
Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex
McCormick, D. A., Connors, B. W., Lighthall, J. W., & Prince, D. A. (1985) · Journal of Neurophysiology · doi:10.1152/jn.1985.54.4.782 · Cited by 1,935
Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity
van Vreeswijk, C., & Sompolinsky, H. (1996) · Science · doi:10.1126/science.274.5293.1724 · Cited by 1,831
A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue
Wilson, H. R., & Cowan, J. D. (1973) · Kybernetik · doi:10.1007/bf00288786 · Cited by 1,823
Modulation of memory fields by dopamine Dl receptors in prefrontal cortex
Williams, G. V., & Goldman-Rakic, P. S. (1995) · Nature · doi:10.1038/376572a0 · Cited by 1,593
Cognitive Deficit Caused by Regional Depletion of Dopamine in Prefrontal Cortex of Rhesus Monkey
Brozoski, T. J., Brown, R. M., Rosvold, H. E., & Goldman, P. S. (1979) · Science · doi:10.1126/science.112679 · Cited by 1,500
Neural Mechanisms of Visual Working Memory in Prefrontal Cortex of the Macaque
Miller, E. K., Erickson, C. A., & Desimone, R. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-16-05154.1996 · Cited by 1,474
Dissociation of Object and Spatial Processing Domains in Primate Prefrontal Cortex
Wilson, F. A. W., Scalaidhe, S. P., & Goldman, P. S. (1993) · Science · doi:10.1126/science.8316836 · Cited by 1,408

Flagged first, then by citations

Show all 73 references

Research works printed but not linked: 5

Destexhe A, Mainen ZF, Sejnowski TJ (1998) Kinetic models of synaptic transmission. In: Koch C, Segev I, eds. Methods in Neuronal Modeling (2nd ed.). MIT Press, Cambridge, MA. pp. 1-25.
Goldman-Rakic PS (1987) Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. In: Handbook of Physiology: The Nervous System V. American Physiological Society, Bethesda, MD. Chapter 9, pp. 373-417.
Hebb DO (1949) Organization of Behavior. Wiley, New York.
Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical Recipes in C. Cambridge University Press, Cambridge.
Tuckwell HC (1988) Introduction to Theoretical Neurobiology. Cambridge University Press, Cambridge.

Sorted by citations

Items 201–300 of 617

Global disruption in excitation-inhibition balance can cause localized network dysfunction and Schizophrenia-like context-integration deficits
Calvin, O. L., & Redish, A. D. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008985 · Cited by 49
Using the Virtual Brain to Reveal the Role of Oscillations and Plasticity in Shaping Brain's Dynamical Landscape
Roy, D., Sigala, R., Breakspear, M. J., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0252 · Cited by 47
The neuronal dynamics underlying cognitive flexibility in set shifting tasks
Stemme, A., Deco, G., & Busch, A. (2007) · Journal of Computational Neuroscience · doi:10.1007/s10827-007-0034-x · Cited by 48
Artificial intelligence technologies for personnel learning management systems
Nenkov, N., Dimitrov, G., Dyachenko, Y., & Koeva, K. (2016) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2016.7737420 · Cited by 48
Geometry of neural computation unifies working memory and planning
Ehrlich, D. B., & Murray, J. D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115610119 · Cited by 48
Amphetamine Exerts Dose-Dependent Changes in Prefrontal Cortex Attractor Dynamics during Working Memory
Lapish, C. C., Balaguer-Ballester, E., Seamans, J. K., Phillips, A. G., & Durstewitz, D. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2421-14.2015 · Cited by 45
Elevated Correlations in Neuronal Ensembles of Mouse Auditory Cortex Following Parturition
Rothschild, G., Cohen, L., Mizrahi, A., & Nelken, I. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4656-12.2013 · Cited by 46
Network Bursting Dynamics in Excitatory Cortical Neuron Cultures Results from the Combination of Different Adaptive Mechanism
Masquelier, T., & Deco, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075824 · Cited by 47
Dopamine Modulation of GABAergic Function Enables Network Stability and Input Selectivity for Sustaining Working Memory in a Computational Model of the Prefrontal Cortex
Lew, S. E., & Tseng, K. Y. (2014) · Neuropsychopharmacology · doi:10.1038/npp.2014.160 · Cited by 46
Hopf Bifurcation in Mean Field Explains Critical Avalanches in Excitation-Inhibition Balanced Neuronal Networks: A Mechanism for Multiscale Variability
Liang, J., Zhou, T., & Zhou, C. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.580011 · Cited by 45
Resting state networks in empirical and simulated dynamic functional connectivity
Glomb, K., Ponce‐Alvarez, A., Gilson, M., Ritter, P., & Deco, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.065 · Cited by 42
Dopamine Modulation of the Prefrontal Cortex and Cognitive Function
Seamans, J. K., & Robbins, T. W. (2009) · Humana Press eBooks · doi:10.1007/978-1-60327-333-6_14 · Cited by 47
Learning in realistic networks of spiking neurons and spike‐driven plastic synapses
Mongillo, G., Curti, E., Romani, S., & Amit, D. J. (2005) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2005.04087.x · Cited by 45
Entropy, free energy, symmetry and dynamics in the brain
Jirsa, V., & Sheheitli, H. (2022) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac4bec · Cited by 49
Multiple forms of working memory emerge from synapse–astrocyte interactions in a neuron–glia network model
Pittà, M. D., & Brunel, N. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207912119 · Cited by 44
Self-citation
Dopamine and gamma band synchrony in schizophrenia – insights from computational and empirical studies
Kömek, K., Ermentrout, B., Walker, C. P. R., & Cho, R. Y. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08071.x · Cited by 46
Environmental enrichment selectively increases glutamatergic responses in layer II/III of the auditory cortex of the rat
Nichols, J., Jakkamsetti, V., Salgado, H., Dinh, L., Kilgard, M. P., & Atzori, M. (2007) · Neuroscience · doi:10.1016/j.neuroscience.2006.12.061 · Cited by 43
Noninvasively Decoding the Contents of Visual Working Memory in the Human Prefrontal Cortex within High-gamma Oscillatory Patterns
Polanía, R., Paulus, W., & Nitsche, M. A. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00151 · Cited by 42
Hippocampal storage and recall of neocortical “What”–“Where” representations
Rolls, E. T., Zhang, C., & Feng, J. (2024) · Hippocampus · doi:10.1002/hipo.23636 · Cited by 43
Dopamine D2 Receptors Enhance Population Dynamics in Primate Prefrontal Working Memory Circuits
Ott, T., & Nieder, A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw244 · Cited by 46
Fractal dimension analysis of states of consciousness and unconsciousness using transcranial magnetic stimulation
de Miras, J. R., Toscano, F. S., Iglesias‐Parro, S., Ibáñez-Molina, A. J., Casali, A. G., Laureys, S., Massimini, M., Esteban, F. J., Navas, J., & Langa, J. A. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.04.017 · Cited by 44
From behavior to circuit modeling of light-seeking navigation in zebrafish larvae
Karpenko, S., Wolf, S., Lafaye, J., Goc, G. L., Panier, T., Bormuth, V., Candelier, R., & Debrégeas, G. (2020) · eLife · doi:10.7554/elife.52882 · Cited by 41
The Contribution of AMPA and NMDA Receptors to Persistent Firing in the Dorsolateral Prefrontal Cortex in Working Memory
van Vugt, B., van Kerkoerle, T., Vartak, D., & Roelfsema, P. R. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2121-19.2020 · Cited by 44
Dynamic primitives of brain network interaction
Schirner, M., Kong, X., Yeo, B. T. T., Deco, G., & Ritter, P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118928 · Cited by 42
Irregular persistent activity induced by synaptic excitatory feedback
Barbieri, F. (2007) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.005.2007 · Cited by 42
Glutamate, obsessive–compulsive disorder, schizophrenia, and the stability of cortical attractor neuronal networks
Rolls, E. T. (2012) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2011.06.017 · Cited by 39
Extracting Information from the Power Spectrum of Synaptic Noise
Alain Destexhe, A., & Rudolph, M. (2004) · Journal of Computational Neuroscience · doi:10.1023/b:jcns.0000044875.90630.88 · Cited by 40
Significance of Input Correlations in Striatal Function
Yim, M. Y., Aertsen, A., & Kumar, A. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002254 · Cited by 45
Networks for memory, perception, and decision-making, and beyond to how the syntax for language might be implemented in the brain
Rolls, E. T., & Deco, G. (2014) · Brain Research · doi:10.1016/j.brainres.2014.09.021 · Cited by 40
Working Memory Cells' Behavior May Be Explained by Cross-Regional Networks with Synaptic Facilitation
Verduzco-Flores, S. O., Bodner, M., Ermentrout, B., Fuster, J. M., & Zhou, Y. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006399 · Cited by 40
Auditory hallucinations
Blom, J. D. (2015) · Handbook of clinical neurology · doi:10.1016/b978-0-444-62630-1.00024-x · Cited by 39
Cognitive consilience: Primate non-primary neuroanatomical circuits underlying cognition
Solari (2011) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2011.00065 · Cited by 41
A Tradeoff Between Accuracy and Flexibility in a Working Memory Circuit Endowed with Slow Feedback Mechanisms
Pereira, J., & Wang, X. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu202 · Cited by 36
Self-citation
Flexible Working Memory Through Selective Gating and Attentional Tagging
Kruijne, W., Bohté, S. M., Roelfsema, P. R., & Olivers, C. N. L. (2020) · Neural Computation · doi:10.1162/neco_a_01339 · Cited by 41
A Tweaking Principle for Executive Control: Neuronal Circuit Mechanism for Rule-Based Task Switching and Conflict Resolution
Ardid, S., & Wang, X. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1356-13.2013 · Cited by 41
Self-citation
Top — Down Control of Visual Perception: Attention in Natural Vision
Rolls, E. T. (2008) · Perception · doi:10.1068/p5877 · Cited by 38
Analytical approximations of the firing rate of an adaptive exponential integrate-and-fire neuron in the presence of synaptic noise
Hertäg, L., Durstewitz, D., & Brunel, N. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00116 · Cited by 36
Self-citation
Functional neuroimaging and schizophrenia: a view towards effective connectivity modeling and polygenic risk
Birnbaum, R., & Weinberger, D. R. (2013) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2013.15.3/rbirnbaum · Cited by 36
Human skill learning: expansion, exploration, selection, and refinement
Lövdén, M., Garzón, B., & Lindenberger, U. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.11.002 · Cited by 43
Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex
Avery, M. C., & Krichmar, J. L. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00031 · Cited by 37
Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes
Yang, Y., Zheng, Z., Liu, L., Zheng, H., Zhen, Y., Zheng, Y., Wang, X., & Tang, S. (2023) · Nature Communications · doi:10.1038/s41467-023-42053-4 · Cited by 35
Self-citation
Memory Capacity of Balanced Networks
Aviel, Y., Horn, D., & Abeles, M. (2005) · Neural Computation · doi:10.1162/0899766053019962 · Cited by 32
Stochastic cortical neurodynamics underlying the memory and cognitive changes in aging
Rolls, E. T., & Deco, G. (2014) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2014.12.003 · Cited by 34
Feature-based attention in human visual cortex: simulation of fMRI data
Corchs, S., & Deco, G. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.08.045 · Cited by 34
Distributed and Associative Working Memory
Zhou, Y.-D., Ardestani, A., & Fuster, J. M. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm106 · Cited by 32
Approaches to cognitive architecture of autonomous intelligent agent
Dyachenko, Y., Nenkov, N., Petrova, M. M., Skarga-Bandurova, I., & Soloviov, O. (2018) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2018.10.004 · Cited by 36
Attractor Network Dynamics, Transmitters, and Memory and Cognitive Changes in Aging
Rolls, E. T. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108554350.014 · Cited by 32
Mnemonic representations of transient stimuli and temporal sequences in the rodent hippocampus in vitro
Hyde, R. A., & Strowbridge, B. W. (2012) · Nature Neuroscience · doi:10.1038/nn.3208 · Cited by 35
Altered inhibition of negative emotions in subjects at family risk of major depressive disorder
Lisiecka, D. M., Carballedo, A., Fagan, A. J., Connolly, G., Meaney, J. F., & Frodl, T. (2011) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2011.10.010 · Cited by 32
Stability of discrete memory states to stochastic fluctuations in neuronal systems
Miller, P. C., & Wang, X. (2006) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.2208923 · Cited by 32
Self-citation
Cooperation and biased competition model can explain attentional filtering in the prefrontal cortex
Szabo, M., Almeida, R., Deco, G., & Stetter, M. (2004) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2004.03211.x · Cited by 32
Structural connectivity allows for multi-threading during rest: The structure of the cortex leads to efficient alternation between resting state exploratory behavior and default mode processing
Senden, M., Goebel, R., & Deco, G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.061 · Cited by 30
Plasticity in the Working Memory System: Life Span Changes and Response to Injury
Froudist‐Walsh, S., López-Barroso, D., Torres‐Prioris, M. J., Croxson, P. L., & Berthier, M. L. (2017) · The Neuroscientist · doi:10.1177/1073858417717210 · Cited by 37
Serotonergic modulation of spatial working memory: predictions from a computational network model
Cano-Colino, M., Almeida, R., & Compte, A. (2013) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2013.00071 · Cited by 34
Speed-accuracy tradeoff by a control signal with balanced excitation and inhibition
Lo, C., Wang, C.-T., & Wang, X. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00845.2013 · Cited by 39
Self-citation
Prediction of Decisions from Noise in the Brain before the Evidence is Provided
Rolls, E. T., & Deco, G. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00033 · Cited by 30
A network of spiking neurons that can represent interval timing: mean field analysis
Gavornik, J. P., & Shouval, H. Z. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0275-y · Cited by 31
A Common Cortical Circuit Mechanism for Perceptual Categorical Discrimination and Veridical Judgment
Liu, F., & Wang, X. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000253 · Cited by 28
Self-citation
Neuronal Adaptation Effects in Decision Making
Theodoni, P., Kovács, G., Greenlee, M. W., & Deco, G. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2757-10.2011 · Cited by 30
Determinants of Brain Rhythm Burst Statistics
Powanwe, A. S., & Longtin, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-54444-z · Cited by 30
Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks
Liang, J., & Zhou, C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009848 · Cited by 27
Neuromodulation and Neurophysiology on the Timescale of Learning and Decision-Making
Grossman, C. D., & Cohen, J. Y. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092021-125059 · Cited by 30
Cortical attractor network dynamics with diluted connectivity
Rolls, E. T., & Webb, T. J. (2011) · Brain Research · doi:10.1016/j.brainres.2011.08.002 · Cited by 31
Decomposing the neural mechanisms of visual search through model-based analysis of fMRI: Top-down excitation, active ignoring and the use of saliency by the right TPJ
Mavritsaki, E., Allen, H. A., & Humphreys, G. W. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.044 · Cited by 28
Nonlinear multiplicative dendritic integration in neuron and network models
Zhang, D., Li, Y., Rasch, M. J., & Wu, S. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00056 · Cited by 29
Patterned firing of parietal cells in a haptic working memory task
Bodner, M., Shafi, M. M., Zhou, Y. ‐., & Fuster, J. M. (2005) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2005.04085.x · Cited by 27
Deterministic analysis of stochastic bifurcations in multi-stable neurodynamical systems
Deco, G., & Marti, D. N. (2007) · Biological Cybernetics · doi:10.1007/s00422-007-0144-6 · Cited by 30
Dimension Reduction and Dynamics of a Spiking Neural Network Model for Decision Making under Neuromodulation
Eckhoff, P. A., Wong‐Lin, K., & Holmes, P. J. (2011) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/090770096 · Cited by 28
Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons
Sanzeni, A., Histed, M. H., & Brunel, N. (2022) · Physical Review X · doi:10.1103/physrevx.12.011044 · Cited by 26
Self-citation
Emergence of canonical functional networks from the structural connectome
Xie, X., Cai, C., Damasceno, P. F., Nagarajan, S. S., & Raj, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118190 · Cited by 24
Dopaminergic Modulation of Prefrontal Cortex Inhibition
Domenico, D. D., & Mapelli, L. (2023) · Biomedicines · doi:10.3390/biomedicines11051276 · Cited by 31
Temporal Properties of Posterior Parietal Neuron Discharges During Working Memory and Passive Viewing
Joelving, F. C., Compte, A., & Constantinidis, C. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00977.2006 · Cited by 27
Cognitive flexibility and decision‐making in a model of conditional visuomotor associations
Loh, M., & Deco, G. (2005) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2005.04505.x · Cited by 26
Controlling Working Memory Operations by Selective Gating: The Roles of Oscillations and Synchrony
Dipoppa, M., Marcin Szwed, A., & Gutkin, B. S. (2016) · Advances in Cognitive Psychology · doi:10.5709/acp-0199-x · Cited by 26
Learning to Attend: Modeling the Shaping of Selectivity in Infero-temporal Cortex in a Categorization Task
Szabo, M., Stetter, M., Deco, G., Fusi, S., Giudice, P. D., & Mattia, M. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0054-z · Cited by 26
A model of dopamine modulated cortical activation
Ashby, F. G., & Casale, M. B. (2003) · Neural Networks · doi:10.1016/s0893-6080(03)00051-0 · Cited by 25
Towards biologically constrained attractor models of schizophrenia
Stein, H., Barbosa, J., & Compte, A. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.10.013 · Cited by 27
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks
Alemi, A., Baldassi, C., Brunel, N., & Zecchina, R. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004439 · Cited by 21
Self-citation
Top-Down Modulation on Perceptual Decision with Balanced Inhibition through Feedforward and Feedback Inhibitory Neurons
Wang, C.-T., Lee, C.-T., Wang, X., & Lo, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062379 · Cited by 23
Self-citation
Decision Time, Slow Inhibition, and Theta Rhythm
Smerieri, A., Rolls, E. T., & Feng, J. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0945-10.2010 · Cited by 25
Non-reward neural mechanisms in the orbitofrontal cortex
Rolls, E. T., & Deco, G. (2016) · Cortex · doi:10.1016/j.cortex.2016.06.023 · Cited by 27
Modeling neuropathologies as disruption of normal sequence generation in working memory networks
Verduzco-Flores, S. O., Ermentrout, B., & Bodner, M. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.09.007 · Cited by 25
The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs
Giugliano, M., Camera, G. L., Fusi, S., & Senn, W. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0270-9 · Cited by 25
Communication before coherence
Rolls, E. T., Webb, T. J., & Deco, G. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08188.x · Cited by 25
Learning Through Imitation: a Biological Approach to Robotics
Chersi, F. (2012) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2012.2200250 · Cited by 24
The memory systems of the human brain and generative artificial intelligence
Rolls, E. T. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31965 · Cited by 23
Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex
Ceccarelli, F., Ferrucci, L., Londei, F., Ramawat, S., Brunamonti, E., & Genovesio, A. (2023) · Nature Communications · doi:10.1038/s41467-023-43712-2 · Cited by 23
Putative dopamine neurons in the ventral tegmental area enhance information coding in the prefrontal cortex
Mininni, C. J., Caiafa, C. F., Zanutto, B. S., Tseng, K. Y., & Lew, S. E. (2018) · Scientific Reports · doi:10.1038/s41598-018-29979-2 · Cited by 21
Inferring Network Dynamics and Neuron Properties from Population Recordings
Linaro, D., Storace, M., & Mattia, M. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00043 · Cited by 24
Cholinergic Modulation Promotes Attentional Modulation in Primary Visual Cortex- A Modeling Study
Sajedin, A., Menhaj, M. B., Vahabie, A., Panzeri, S., & Esteky, H. (2019) · Scientific Reports · doi:10.1038/s41598-019-56608-3 · Cited by 22
A Neurodynamical Model of Brightness Induction in V1
Penacchio, O., Otazu, X., & Dempere‐Marco, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064086 · Cited by 21
Dopamine firing plays a dual role in coding reward prediction errors and signaling motivation in a working memory task
Sarno, S., Beirán, M., Falcó-Roget, J., Diaz-deLeon, G., Rossi-Pool, R., Romo, R., & Parga, N. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113311119 · Cited by 22
Cortical signal-to-noise ratio: insight into the pathophysiology and genetics of schizophrenia
Winterer, G., & Weinberger, D. R. (2003) · Clinical Neuroscience Research · doi:10.1016/s1566-2772(03)00019-7 · Cited by 21
Simulation of the capacity and precision of working memory in the hypodopaminergic state: Relevance to schizophrenia
Okimura, T., Tanaka, S., Maeda, T., Kato, M., & Mimura, M. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.03.039 · Cited by 21
Temporal chunking as a mechanism for unsupervised learning of task-sets
Bouchacourt, F., Palminteri, S., Koechlin, É., & Ostojic, S. (2020) · eLife · doi:10.7554/elife.50469 · Cited by 22
Clinical Applications of Stochastic Dynamic Models of the Brain, Part II: A Review
Roberts, J. A., Friston, K. J., & Breakspear, M. J. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.12.009 · Cited by 21
Co-operative Populations of Neurons: Mean Field Models of Mesoscopic Brain Activity
Liley, D. T. J., Foster, B. L., & Bojak, I. (2012) · Springer eBooks · doi:10.1007/978-94-007-3858-4_11 · Cited by 21
A microcircuit model of prefrontal functions: ying and yang of reverberatory neurodynamics in cognition
Wang, X. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511545917.006 · Cited by 21
Self-citation
Dopaminergic neuromodulation of semantic priming in a cortical network model
Lavigne, F., & Darmon, N. (2008) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2008.06.019 · Cited by 21
Non-equilibrium landscape and flux reveal the stability-flexibility-energy tradeoff in working memory
Yan, H., & Wang, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008209 · Cited by 20

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

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 ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks