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Large-scale recording of neuronal ensembles
Abstract
How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
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“The primary goal of such studies is to understand how the activities of groups of neurons are related to sensation, behavior and cognition [14, 117].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [14]
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Cites
The Organization of Behavior; A Neuropsychological Theory.
Attneave, F., B., M., & Hebb, D. O. (1950) · The American Journal of Psychology · doi:10.2307/1418888 · Cited by 5,146
The Organization of Behavior: A Neuropsychological Theory
(1950) · JAMA · doi:10.1001/jama.1950.02910470083028 · Cited by 4,272
Gray, C. M., König, P., Engel, A. K., & Singer, W. (1989) · Nature · doi:10.1038/338334a0 · Cited by 4,183
Dynamic predictions: Oscillations and synchrony in top–down processing
Engel, A. K., Fries, P., & Singer, W. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094565 · Cited by 3,454
Dynamics of the Hippocampal Ensemble Code for Space
Wilson, M. A., & McNaughton, B. L. (1993) · Science · doi:10.1126/science.8351520 · Cited by 2,350
Llinás, R. R. (1988) · Science · doi:10.1126/science.3059497 · Cited by 2,285
Shadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178
Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y. (2004) · Neural Computation · doi:10.1162/089976604774201631 · Cited by 2,119
Learning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761
Churchland, P. S., & Sejnowski, T. J. (1992) · doi:10.7551/mitpress/2010.001.0001 · Cited by 1,757
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2 of the 64 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Wise, K.D. Micromachined Interfaces to the cellular world. Sensors Materials 10, 385–395 (1998).
- Buzsáki, G., Traub, R.D. & Pedley, T. The cellular synaptic generation of EEG. in Current Practice of Clinical Encephalography Edn. 3 (eds. Ebersole, J.S. & Pedley, T.A.) 1–11 (Lippincott Williams & Wilkins, Philadelphia, 2003).
- Eichenbaum, H. & Davis, J.L. Neuronal Ensembles: Strategies for Recording and Coding (Wiley-Liss, New York, 1988).
- Olsson III, R.H., Buhl, D.L., Gulari, M.N., Buzsaki, G. & Wise, K.D. A silicon microelectrode array for simultaneous recording and stimulation in the hippocampus of free moving rats and mice. IEEE Eng. Med. Biol. Mag. 22, 1968–1671, 2003.
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Finding neural assemblies with frequent item set mining
Muiño, D. P., Borgelt, C., Berger, D. J., Gerstein, G. L., & Grün, S. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00009 · Cited by 43
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Mandal, H. S., Kastee, J. S., McHail, D. G., Rubinson, J. F., Pancrazio, J. J., & Dumas, T. C. (2015) · Neuromodulation Technology at the Neural Interface · doi:10.1111/ner.12285 · Cited by 43
Angelov, S. D., Koenen, S., Jakobi, J., Heissler, H. E., Alam, M., Schwabe, K., Barcikowski, S., & Krauss, J. K. (2016) · Journal of Nanobiotechnology · doi:10.1186/s12951-015-0154-9 · Cited by 43
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Lambert, R. C., Tuleau-Malot, C., Bessaïh, T., Rivoirard, V., Bouret, Y., Leresche, N., & Reynaud-Bouret, P. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.12.026 · Cited by 54
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Ness, T. V., Remme, M. W. H., & Einevoll, G. T. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3278-17.2018 · Cited by 43
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Tomsett, R., Ainsworth, M., Thiele, A., Sanayei, M., A Gieselmann, M., Whittington, M. A., O Cunningham, M., & Kaiser, M. (2014) · Figshare · doi:10.6084/m9.figshare.1088264 · Cited by 43
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Velázquez, J. L. P. (2005) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2005.10.005 · Cited by 42
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Lourens, M. A. J., Meijer, H. G. E., Contarino, M. F., van den Munckhof, P., Schuurman, P. R., van Gils, S. A., & Bour, L. J. (2012) · Clinical Neurophysiology · doi:10.1016/j.clinph.2012.10.018 · Cited by 42
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Sanchez, J. C., & Príncipe, J. C. (2007) · Synthesis lectures on biomedical engineering · doi:10.1007/978-3-031-01621-9 · Cited by 42
Sharma, G., Annetta, N., Friedenberg, D., Blanco, T., Vasconcelos, D., Shaikhouni, A., Rezai, A. R., & Bouton, C. (2015) · Bioelectronic Medicine · doi:10.15424/bioelectronmed.2015.00010 · Cited by 41
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Zhang, T., Azghadi, M. R., Lammie, C., Amirsoleimani, A., & Genov, R. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acc7cc · Cited by 42
Neural codes in the thalamocortical auditory system: From artificial stimuli to communication sounds
Huetz, C., Gourévitch, B., & Edeline, J. (2010) · Hearing Research · doi:10.1016/j.heares.2010.01.010 · Cited by 40
Estimation of genuine and random synchronization in multivariate neural series
Cui, D. Y., Liu, X., Wan, Y., & Xiaoli Li, A. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.04.003 · Cited by 40
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Gao, K., Li, S., Zhuang, L., Qin, Z., Zhang, B., Huang, L., & Wang, P. (2017) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2017.08.055 · Cited by 41
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Laboy-Juárez, K. J., Ahn, S., & Feldman, D. E. (2019) · Scientific Reports · doi:10.1038/s41598-019-48456-y · Cited by 41
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Yang, Z. Q., Zhao, Q., & Liu, W. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/4/046006 · Cited by 42
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Juusola, M., Robinson, H. P. C., & de Polavieja, G. G. (2007) · BioEssays · doi:10.1002/bies.20532 · Cited by 40
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Nguyen, T. T., Khosravi, A., Creighton, D., & Nahavandi, S. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.09.011 · Cited by 39
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Böhler, C., Vomero, M., Soula, M., Vöröslakos, M., Cruz, M. F. P., Liljemalm, R., Buzsáki, G., Stieglitz, T., & Asplund, M. (2023) · Advanced Science · doi:10.1002/advs.202207576 · Cited by 38
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Morris, R. (2012) · Springer eBooks · doi:10.1007/978-1-4614-1997-6_81 · Cited by 37
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Hermann, J. K., Ravikumar, M., Shoffstall, A. J., Ereifej, E. S., Kovach, K. M., Chang, J. B., Soffer, A., Wong, C., Ali, A., Smith, P., et al. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaa03e · Cited by 38
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Ziemba, C. M., Perez, R. K., Pai, J., Hallum, L. E., Shooner, C., Kelly, J. G., & Movshon, J. A. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1743-19.2019 · Cited by 38
Dutta, B., Andrei, A., Harris, T. D., López, C. M., O’Callahan, J., Putzeys, J., Raducanu, B. C., Severi, S., Stavisky, S. D., Trautmann, E. M., et al. (2019) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm19573.2019.8993611 · Cited by 37
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Pazhouhandeh, M. R., Kassiri, H., Shoukry, A., Weisspapir, I., Carlen, P. L., & Genov, R. (2020) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2020.3041289 · Cited by 37
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Fang, J., Huang, S., Liu, F., He, G., Li, X., Huang, X., Chen, H., & Xie, X. (2022) · Nano-Micro Letters · doi:10.1007/s40820-022-00818-4 · Cited by 37
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Das, T. K., Abeyasinghe, P. M., Crone, J. S., Sosnowski, A., Laureys, S., Owen, A. M., & Soddu, A. (2014) · BioMed Research International · doi:10.1155/2014/237898 · Cited by 38
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Polanco, M., Bawab, S., & Yoon, H. (2016) · Biosensors · doi:10.3390/bios6020027 · Cited by 36
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Franke, F., Pröpper, R., Alle, H., Meier, P., Geiger, J. R. P., Obermayer, K., & Munk, M. H. J. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00993.2014 · Cited by 36
Goshi, N., Castagnola, E., Vomero, M., Gueli, C., Cea, C., Zucchini, E., Bjånes, D. A., Maggiolini, E., Moritz, C. T., Kassegne, S., et al. (2018) · Journal of Micromechanics and Microengineering · doi:10.1088/1361-6439/aab061 · Cited by 39
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Makino, Y., Polygalov, D., Bolaños, F., Benucci, A., & McHugh, T. J. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.075 · Cited by 38
Joseph, K., Kirsch, M., Johnston, M., Münkel, C., Stieglitz, T., Haas, C. A., & Hofmann, U. (2021) · Biomaterials · doi:10.1016/j.biomaterials.2021.121230 · Cited by 36
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Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., & Vassanelli, S. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.03.009 · Cited by 36
Direct neural current imaging in an intact cerebellum with magnetic resonance imaging
Sundaram, P., Nummenmaa, A. R., Wells, W. M., Orbach, D. B., Orringer, D. A., Mulkern, R. V., & Okada, Y. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.059 · Cited by 36
Low and high frequency intracranial neural signals match in the human associative cortex
Jacques, C., Jonas, J., Colnat‐Coulbois, S., Maillard, L. G., & Rossion, B. (2022) · eLife · doi:10.7554/elife.76544 · Cited by 37
Jeon, W., Lee, J. M., Kim, Y., Lee, Y., Won, J., Lee, S., Son, W., Koo, Y. H., Hong, J., Gwac, H., et al. (2024) · Advanced Materials · doi:10.1002/adma.202313625 · Cited by 36
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Oh, S., Jekal, J., Won, J., Lim, K. S., Jeon, C., Park, J., Yeo, H.-G., Kim, Y. G., Lee, Y. H., Ha, L. J., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01280-w · Cited by 35
Bjånes, D. A., Kellis, S., Nickl, R., Baker, B., Aflalo, T., Bashford, L., Chivukula, S., Fifer, M. S., Osborn, L. E., Christie, B., et al. (2025) · Acta Biomaterialia · doi:10.1016/j.actbio.2025.02.030 · Cited by 37
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Dubey, A., & Ray, S. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00443.2016 · Cited by 36
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Battaglia, F. P., Sutherland, G. R., Cowen, S. L., Naughton, B. L. M., & Harris, K. D. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.08.011 · Cited by 35
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Zhou, D., Xiao, Y., Zhang, Y., Xu, Z. J., & Cai, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087636 · Cited by 37
Brown, E. C., Muzik, O., Rothermel, R., Matsuzaki, N., Juhász, C., Shah, A. K., Atkinson, M. D., Fuerst, D. R., Mittal, S., Sood, S., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.040 · Cited by 35
Vizuete, J. A., Pillay, S., Diba, K., ROPELLA, K. M., & Hudetz, A. G. (2012) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2012.00090 · Cited by 37
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Wang, P., Wu, E. G., Uluşan, H., Zhao, E. T., Phillips, A., Kling, A., Hays, M. R., Vasireddy, P., Madugula, S. S., Vilkhu, R. S., et al. (2024) · Advanced Science · doi:10.1002/advs.202408602 · Cited by 38
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Sanfratello, L., Houck, J. M., & Calhoun, V. D. (2019) · Brain Connectivity · doi:10.1089/brain.2018.0608 · Cited by 34
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Gutierrez, G. J., & Denève, S. (2019) · eLife · doi:10.7554/elife.46926 · Cited by 35
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Schwindel, C. D., Ali, K., McNaughton, B. L., & Tatsuno, M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4350-13.2014 · Cited by 35
Ohiorhenuan, I. E., & Victor, J. D. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0257-0 · Cited by 34
Micro-drive and headgear for chronic implant and recovery of optoelectronic probes
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Neural-Event-Triggered fMRI of large-scale neural networks
Logothetis, N. K. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.11.009 · Cited by 34
Multichannel Silicon Probes for Awake Hippocampal Recordings in Large Animals
Ulyanova, A. V., Cottone, C., Adam, C. D., Gagnon, K. G., Cullen, D. K., Holtzman, T., Jamieson, B. G., Koch, P. F., Chen, H., Johnson, V. E., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00397 · Cited by 33
A lightweight, wireless Bluetooth-low-energy neuronal recording system for mice
Idogawa, S., Yamashita, K., Sanda, R., Numano, R., Koida, K., & Kawano, T. (2021) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2020.129423 · Cited by 33
Paraskevopoulou, S. E., Wu, D., Eftekhar, A., & Constandinou, T. G. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.07.004 · Cited by 33
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Kawano, T., Harimoto, T., Ishihara, A., Takei, K., Kawashima, T., Usui, S., & Ishida, M. (2010) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2009.12.037 · Cited by 33
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Metadata
- Authors
- Buzsáki, G.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Buzsáki, György
- ORCID
- Buzsáki, György
- Venue
- Nature Neuroscience
- Published
- 2004-04-27
- Volume
- 7
- Issue
- 5
- Pages
- 446-451
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2007495909
- Cited by
- 1,875
- References
- 64
- Citations by year
- 2026: 59 · 2025: 105 · 2024: 88 · 2023: 94 · 2022: 89 · 2021: 98 · 2020: 92 · 2019: 119 · 2018: 102 · 2017: 111 · 2016: 99 · 2015: 102 · 2014: 108 · 2013: 98 · 2012: 65
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5p41rr009754-09)
- National Institute of Mental Health (R01MH054671, MH54671)
- National Institute of Neurological Disorders and Stroke (NS 43157, R01NS043157, R01NS034994, NS-34994)
- National Center for Research Resources (1P41RR-09754)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- neuronal ensembles, large-scale neural recording, spike sorting, cortical circuits, neural network analysis, single-unit recording
- MeSH terms
- Action Potentials, Animals, Brain, Electrophysiology, Humans, Microelectrodes, Nerve Net, Neurons, Neurophysiology
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