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Working Memory 2.0
Open accessAbstract
Working memory is the fundamental function by which we break free from reflexive input-output reactions to gain control over our own thoughts. It has two types of mechanisms: online maintenance of information and its volitional or executive control. Classic models proposed persistent spiking for maintenance but have not explicitly addressed executive control. We review recent theoretical and empirical studies that suggest updates and additions to the classic model. Synaptic weight changes between sparse bursts of spiking strengthen working memory maintenance. Executive control acts via interplay between network oscillations in gamma (30-100 Hz) in superficial cortical layers (layers 2 and 3) and alpha and beta (10-30 Hz) in deep cortical layers (layers 5 and 6). Deep-layer alpha and beta are associated with top-down information and inhibition. It regulates the flow of bottom-up sensory information associated with superficial layer gamma. We propose that interactions between different rhythms in distinct cortical layers underlie working memory maintenance and its volitional control.
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“A key observation is that, all across cortex, gamma power (>35 Hz)/spiking is higher during bottom-up sensory inputs.”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [29]
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“They are anticorrelated with alpha/beta (8 to 30 Hz) power (26–29), which is higher under conditions of top-down control (e.g., attention and response inhibition) (30–34).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [29]
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Cites
An Integrative Theory of Prefrontal Cortex Function
Miller, E. K., & Cohen, J. D. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.167 · Cited by 12,803
Self-citationNeural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
The capacity of visual working memory for features and conjunctions
Luck, S. J., & Vogel, E. K. (1997) · Nature · doi:10.1038/36846 · Cited by 4,265
A capacity theory of comprehension: individual differences in working memory
Just, M. A., & Carpenter, P. A. (1992) · Psychological Review · doi:10.1037/0033-295x.99.1.122 · Cited by 4,153
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,537
Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition
Jensen, O., & Mazaheri, A. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00186 · Cited by 3,237
Working memory, short-term memory, and general fluid intelligence: A latent-variable approach.
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A. (1999) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.128.3.309 · Cited by 3,078
Mechanisms of Gamma Oscillations
Buzsáki, G., & Wang, X.-J. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150444 · Cited by 2,796
Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention
Fries, P., Reynolds, J. H., Rorie, A. E., & Desimone, R. (2001) · Science · doi:10.1126/science.1055465 · Cited by 2,786
Flagged first, then by citations
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Cognitive impairment in schizophrenia: aetiology, pathophysiology, and treatment
McCutcheon, R. A., Keefe, R. S. E., & McGuire, P. K. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01949-9 · Cited by 861
Computation Through Neural Population Dynamics
Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092619-094115 · Cited by 671
Schmidt, R., Ruiz, M. H., Kilavik, B. E., Lundqvist, M., Starr, P. A., & Aron, A. R. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1163-19.2019 · Cited by 340
Self-citationLayer and rhythm specificity for predictive routing
Bastos, A. M., Lundqvist, M., Waite, A. S., Kopell, N., & Miller, E. K. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2014868117 · Cited by 268
“They are anticorrelated with alpha/beta (8 to 30 Hz) power (26 – 29), which is higher under conditions of top-down control (e.g., attention and response inhibition) (30 – 34).”
Self-citationRolls, E. T. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02644-9 · Cited by 356
Methodological considerations for studying neural oscillations
Donoghue, T., Schaworonkow, N., & Voytek, B. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15361 · Cited by 309
The population doctrine in cognitive neuroscience
Ebitz, R. B., & Hayden, B. Y. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.011 · Cited by 286
State-dependent effects of neural stimulation on brain function and cognition
Bradley, C., Nydam, A. S., Dux, P. E., & Mattingley, J. B. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00598-1 · Cited by 263
The mouse prefrontal cortex: Unity in diversity
Le Merre, P., Ährlund-Richter, S., & Carlén, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.035 · Cited by 207
Neural effects of propofol-induced unconsciousness and its reversal using thalamic stimulation
Bastos, A. M., Donoghue, J. A., Brincat, S. L., Mahnke, M., Yanar, J., Correa, J., Waite, A. S., Lundqvist, M., Roy, J., Brown, E. N., et al. (2021) · eLife · doi:10.7554/elife.60824 · Cited by 166
Self-citationThe Meaning of Behavior: Discriminating Reflex and Volition in the Brain
Balleine, B. W. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.024 · Cited by 186
Lundqvist, M., Miller, E. K., Nordmark, J., Liljefors, J., & Herman, P. A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.03.010 · Cited by 189
Self-citationControl of working memory by phase–amplitude coupling of human hippocampal neurons
Daume, J., Kamiński, J., Gómez-Palacio-Schjetnan, A., Salimpour, Y., Khan, U., Kyzar, M., Reed, C. M., Anderson, W. S., Valiante, T. A., Mamelak, A. N., et al. (2024) · Nature · doi:10.1038/s41586-024-07309-z · Cited by 187
Oscillatory Control over Representational States in Working Memory
de Vries, I. E. J., Slagter, H. A., & Olivers, C. N. L. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.11.006 · Cited by 168
The role of gamma oscillations in central nervous system diseases: Mechanism and treatment
Guan, A., Wang, S., Huang, A., Qiu, C., Li, Y., Li, X., Jinfei, W., Wang, Q., & Deng, B. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.962957 · Cited by 178
The what, where and how of delay activity
Sreenivasan, K. K., & D’Esposito, M. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0176-7 · Cited by 168
Distraction in Visual Working Memory: Resistance is Not Futile
Lorenc, E. S., Mallett, R., & Lewis‐Peacock, J. A. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.12.004 · Cited by 154
Mechanisms of distributed working memory in a large-scale network of macaque neocortex
Mejías, J. F., & Wang, X. (2022) · eLife · doi:10.7554/elife.72136 · Cited by 136
Translational opportunities and challenges of invasive electrodes for neural interfaces
Shen, K., Chen, O., Edmunds, J. L., Piech, D. K., & Maharbiz, M. M. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01021-5 · Cited by 150
Zheng, J., Stevenson, R. F., Mander, B. A., Mnatsakanyan, L., Hsu, F. P. K., Vadera, S., Knight, R. T., Yassa, M. A., & Lin, J. J. (2018) · Neuron · doi:10.1016/j.neuron.2019.03.025 · Cited by 121
Multiple time-scales of decision-making in the hippocampus and prefrontal cortex
Tang, W., Shin, J. D., & Jadhav, S. P. (2021) · eLife · doi:10.7554/elife.66227 · Cited by 142
Traveling waves in the prefrontal cortex during working memory
Bhattacharya, S., Brincat, S. L., Lundqvist, M., & Miller, E. K. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009827 · Cited by 121
Self-citationA ubiquitous spectrolaminar motif of local field potential power across the primate cortex
Mendoza-Halliday, D., Major, A. J., Lee, N., Lichtenfeld, M. J., Carlson, B. M., Mitchell, B., Meng, P. D., Xiong, Y., Westerberg, J. A., Jia, X., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01554-7 · Cited by 126
Self-citationGamma Oscillations in Alzheimer’s Disease and Their Potential Therapeutic Role
Traikapi, A., & Konstantinou, N. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.782399 · Cited by 123
Dynamic causal brain circuits during working memory and their functional controllability
Cai, W., Ryali, S., Pasumarthy, R., Talasila, V., & Menon, V. (2021) · Nature Communications · doi:10.1038/s41467-021-23509-x · Cited by 105
Cholinergic suppression of hippocampal sharp-wave ripples impairs working memory
Zhang, Y., Cao, L., Varga, V., Jing, M., Karadas, M., Li, Y., & Buzsáki, G. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016432118 · Cited by 111
Brain rhythms define distinct interaction networks with differential dependence on anatomy
Vezoli, J., Vinck, M. A., Bosman, C. A., Bastos, A. M., Lewis, C., Kennedy, H., & Fries, P. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.052 · Cited by 93
Self-citationThe cerebellum and cognitive neural networks
Zhang, P., Duan, L., Ou, Y., Ling, Q., Cao, L., Qian, H., Zhang, J., Wang, J., & Yuan, X. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1197459 · Cited by 115
Cho, K. K. A., Davidson, T. J., Bouvier, G., Marshall, J. D., Schnitzer, M. J., & Sohal, V. S. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0647-1 · Cited by 109
Neural Mechanisms of Domain-general Inhibitory Control
Wessel, J. R., & Anderson, M. C. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.09.008 · Cited by 117
Is Activity Silent Working Memory Simply Episodic Memory?
Beukers, A. O., Buschman, T. J., Cohen, J. D., & Norman, K. A. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.01.003 · Cited by 99
Reevaluating the Role of Persistent Neural Activity in Short-Term Memory
Masse, N. Y., Rosen, M. C., & Freedman, D. J. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.12.014 · Cited by 84
Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages
Prat, C. S., Madhyastha, T. M., Mottarella, M., & Kuo, C.-H. (2020) · Scientific Reports · doi:10.1038/s41598-020-60661-8 · Cited by 94
Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition
Wang, X.-J. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-110920-035434 · Cited by 83
Gamma oscillations and episodic memory
Griffiths, B., & Jensen, O. B. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2023.07.003 · Cited by 85
Roelfsema, P. R. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.016 · Cited by 85
Spontaneous Beta Band Rhythms in the Predictive Coding of Natural Stimuli
Betti, V., Penna, S. D., de Pasquale, F., & Corbetta, M. (2020) · The Neuroscientist · doi:10.1177/1073858420928988 · Cited by 97
Electrophysiological signatures of memory reactivation in humans
Schreiner, T., & Staudigl, T. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0293 · Cited by 87
Prefrontal cortex and cognitive control: new insights from human electrophysiology
Widge, A. S., Heilbronner, S. R., & Hayden, B. Y. (2019) · F1000Research · doi:10.12688/f1000research.20044.1 · Cited by 78
A Connectomic Hypothesis for the Hominization of the Brain
Changeux, J., Goulas, A., & Hilgetag, C. C. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa365 · Cited by 80
Exercise types and working memory components during development
Ludyga, S., Gerber, M., & Kamijo, K. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.12.004 · Cited by 86
Stable maintenance of multiple representational formats in human visual short-term memory
Liu, J., Zhang, H., Yu, T., Ni, D., Ren, L., Yang, Q., Lu, B., Wang, D., Heinen, R. M., Axmacher, N., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006752117 · Cited by 75
Ruotsalainen, I., Gorbach, T., Perkola, J., Renvall, V., Syväoja, H. J., Tammelin, T., Karvanen, J., & Parviainen, T. M. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100765 · Cited by 75
Working memory control dynamics follow principles of spatial computing
Lundqvist, M., Brincat, S. L., Rose, J., Warden, M. R., Buschman, T. J., Miller, E. K., & Herman, P. A. (2023) · Nature Communications · doi:10.1038/s41467-023-36555-4 · Cited by 79
Self-citationStieger, J., Engel, S. A., Jiang, H., Cline, C. C., Kreitzer, M. J., & He, B. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa234 · Cited by 67
Age-Related Differences in Resting-State EEG and Allocentric Spatial Working Memory Performance
Jabès, A., Klencklen, G., Ruggeri, P., Antonietti, J., Lavenex, P. B., & Lavenex, P. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.704362 · Cited by 68
Visual mental imagery: Evidence for a heterarchical neural architecture
Spagna, A., Heidenry, Z., Miselevich, M., Lambert, C. J., Eisenstadt, B. E., Tremblay, L., Liu, Z., Liu, J., & Bartolomeo, P. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.12.012 · Cited by 73
Prefrontal pyramidal neurons are critical for all phases of working memory
Vogel, P., Hahn, J., Duvarci, S., & Sigurdsson, T. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110659 · Cited by 64
Perspectives on adaptive dynamical systems
Sawicki, J., Berner, R., Loos, S. A. M., Anvari, M., Bader, R., Barfuß, W., Botta, N., Brede, N., Franović, I., Gauthier, D. J., et al. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0147231 · Cited by 72
In vivo ephaptic coupling allows memory network formation
Pinotsis, D. A., & Miller, E. K. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad251 · Cited by 62
Self-citationGonzález‐Burgos, G., Miyamae, T., Krimer, Y., Gulchina, Y., Pafundo, D. E., Krimer, O. L., Bazmi, H. H., Arion, D., Enwright, J. F., Fish, K. N., et al. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1210-19.2019 · Cited by 63
The role of executive function in shaping reinforcement learning
Rmus, M., McDougle, S. D., & Collins, A. G. E. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.10.003 · Cited by 61
Cytoelectric coupling: Electric fields sculpt neural activity and “tune” the brain’s infrastructure
Pinotsis, D. A., Fridman, G. Y., & Miller, E. K. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102465 · Cited by 68
Self-citationHannah, R., Muralidharan, V., Sundby, K. K., & Aron, A. R. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117222 · Cited by 64
Stokes, M. G., Muhle-Karbe, P. S., & Myers, N. E. (2020) · Visual Cognition · doi:10.1080/13506285.2020.1825141 · Cited by 60
The neuroscience of active learning and direct instruction
Dubinsky, J. M., & Hamid, A. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105737 · Cited by 121
Auditory cortical connectivity in humans
Rolls, E. T., Rauschecker, J. P., Deco, G., Huang, C., & Feng, J. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac496 · Cited by 56
Jackson, J. B., Feredoes, E. A., Rich, A. N., Lindner, M., & Woolgar, A. (2021) · Communications Biology · doi:10.1038/s42003-021-02109-x · Cited by 56
Long-term learning transforms prefrontal cortex representations during working memory
Miller, J. A., Tambini, A., Kiyonaga, A., & D’Esposito, M. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.019 · Cited by 63
Working Memory Is Complex and Dynamic, Like Your Thoughts
Buschman, T. J., & Miller, E. K. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01940 · Cited by 55
Self-citationOlaru, M., Cernera, S., Hahn, A., Wozny, T. A., Ansó, J., de Hemptinne, C., Little, S. J., Neumann, W., Abbasi-Asl, R., & Starr, P. A. (2024) · Brain · doi:10.1093/brain/awae004 · Cited by 55
Exact neural mass model for synaptic-based working memory
Taher, H., Torcini, A., & Olmi, S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008533 · Cited by 60
Chang, C. H. C., Lazaridi, C., Yeshurun, Y., Norman, K. A., & Hasson, U. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01707 · Cited by 52
Bistability of prefrontal states gates access to consciousness
Dwarakanath, A., Kapoor, V., Werner, J., Safavi, S., Fedorov, L., Logothetis, N. K., & Panagiotaropoulos, T. I. (2023) · Neuron · doi:10.1016/j.neuron.2023.02.027 · Cited by 63
Movement-related beta ERD and ERS abnormalities in neuropsychiatric disorders
Peter, J., Ferraioli, F., Mathew, D. M., George, S., Chan, C., Alalade, T., Salcedo, S. A., Saed, S., Tatti, E., Quartarone, A., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1045715 · Cited by 52
Human connectome topology directs cortical traveling waves and shapes frequency gradients
Koller, D. P., Schirner, M., & Ritter, P. (2024) · Nature Communications · doi:10.1038/s41467-024-47860-x · Cited by 58
Tracking Selective Rehearsal and Active Inhibition of Memory Traces in Directed Forgetting
Fellner, M.-C., Waldhauser, G. T., & Axmacher, N. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.091 · Cited by 55
A Hierarchy of Functional States in Working Memory
Muhle-Karbe, P. S., Myers, N. E., & Stokes, M. G. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.3104-20.2021 · Cited by 51
Corticostriatal beta oscillation changes associated with cognitive function in Parkinson’s disease
Paulo, D. L., Qian, H., Subramanian, D., Johnson, G. W., Zhao, Z., Hett, K., Kang, H., Kao, C., Roy, N. C., Summers, J. E., et al. (2023) · Brain · doi:10.1093/brain/awad206 · Cited by 49
Robust and brain-like working memory through short-term synaptic plasticity
Kozachkov, L., Tauber, J., Lundqvist, M., Brincat, S. L., Slotine, J.-J. E., & Miller, E. K. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010776 · Cited by 51
Self-citationNeuronal Correlates of Spatial Working Memory in the Endbrain of Crows.
Rinnert, P., Kirschhock, M. E., & Nieder, A. (2019) · Current Biology · doi:10.1016/j.cub.2019.06.060 · Cited by 44
Predictive Coding: A more cognitive process than we thought?
Gabhart, K. M., Xiong, Y. (., & Bastos, A. M. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2025.01.012 · Cited by 58
Self-citationBilingual Language Experience Shapes Resting-State Brain Rhythms
Bice, K., Yamasaki, B. L., & Prat, C. S. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00014 · Cited by 46
Dissecting the neurobiology of linguistic disorganisation and impoverishment in schizophrenia
Palaniyappan, L. K. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.08.015 · Cited by 53
Anterior–Posterior Hippocampal Dynamics Support Working Memory Processing
Li, J., Cao, D., Dimakopoulos, V., Shi, W., Yu, S., Fan, L., Stieglitz, L. H., Imbach, L., Sarnthein, J., & Jiang, T. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1287-21.2021 · Cited by 49
Pittà, M. D., & Brunel, N. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207912119 · Cited by 44
Sanchez-Todo, R., Bastos, A. M., Lopez-Sola, E., Mercadal, B., Santarnecchi, E., Miller, E. K., Deco, G., & Ruffini, G. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119938 · Cited by 46
Self-citationReframing the debate: The distributed systems view of working memory
Lorenc, E. S., & Sreenivasan, K. K. (2021) · Visual Cognition · doi:10.1080/13506285.2021.1899091 · Cited by 44
Disentangling the flow of signals between populations of neurons
Gokcen, E., Jasper, A. I., Semedo, J. D., Zandvakili, A., Kohn, A., Machens, C. K., & Yu, B. M. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00282-5 · Cited by 46
A model of working memory for latent representations
Hedayati, S., O'Donnell, R. E., & Wyble, B. (2022) · Nature Human Behaviour · doi:10.1038/s41562-021-01264-9 · Cited by 48
Güntürkün, O., Pusch, R., & Rose, J. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.11.002 · Cited by 48
Human amygdala compared to orbitofrontal cortex connectivity, and emotion
Rolls, E. T., Deco, G., Huang, C., & Feng, J. (2022) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2022.102385 · Cited by 44
Marié, D., Müller, C. A. H., ALTENMÜLLER, E. O., Ville, D. V. D., Jünemann, K., Scholz, D. S., Krüger, T. H. C., Worschech, F., Kliegel, M., Sinke, C., et al. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100166 · Cited by 48
Mental programming of spatial sequences in working memory in the macaque frontal cortex
Tian, Z., Chen, J., Zhang, C., Min, B., Xu, B., & Wang, L. (2024) · Science · doi:10.1126/science.adp6091 · Cited by 47
Targeting Cognition and Networks Through Neural Oscillations
Widge, A. S., & Miller, E. K. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.0740 · Cited by 39
Self-citationWorking memory capacity of crows and monkeys arises from similar neuronal computations
Hahn, L. A., Balakhonov, D., Fongaro, E., Nieder, A., & Rose, J. (2021) · eLife · doi:10.7554/elife.72783 · Cited by 42
Olsen, S. T., Basu, I., Bilge, M. T., Kanabar, A., Boggess, M. J., Rockhill, A. P., Gosai, A. K., Hahn, E. M., Peled, N., Ennis, M. M., et al. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.569973 · Cited by 38
When the time is right: Temporal dynamics of brain activity in healthy aging and dementia
Courtney, S. M., & Hinault, T. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102076 · Cited by 40
Distinct functions for beta and alpha bursts in gating of human working memory
Liljefors, J., Almeida, R., Rane, G., Lundström, J. N., Herman, P. A., & Lundqvist, M. (2024) · Nature Communications · doi:10.1038/s41467-024-53257-7 · Cited by 44
Self-citationLuo, D., Wang, C., Liang, J., & Deng, Q. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-11603-4 · Cited by 40
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Metadata
- Authors
- Miller, E. K., Lundqvist, M., & Bastos, A. M.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Miller, Earl K.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 100
- Issue
- 2
- Pages
- 463-475
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC8112390
- OpenAlex
- W2898524098
- Cited by
- 797
- References
- 165
- Citations by year
- 2026: 100 · 2025: 130 · 2024: 134 · 2023: 143 · 2022: 116 · 2021: 87 · 2020: 63 · 2019: 22 · 2018: 1
- FWCI
- 5.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIMH (R37MH087027)
- ONR (MURI N00014-16-1-2832)
- The Picower Fellows Program
- MIT Picower Institute
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- working memory, working memory maintenance, executive function, synaptic plasticity, gamma oscillations, alpha oscillations, beta oscillations, cortical layers, top-down control, neural oscillations
- MeSH terms
- Animals, Brain, Humans, Memory, Short-Term, Models, Neurological
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