The DOI matches its Crossref record; 161 of the 162 references it cites were found in a registry or an index.
The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes
Open accessAbstract
Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In contrast, ECoG electrodes are generally placed on the surface of the cerebral cortex and can collect signals without penetrating the tissue, with a larger recording area with relatively less damage to the brain.[270]”
From Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics (2022) · cites this paper as [270]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Subcutaneous invasive electrophysiological signals can be derived through brain interfaces,[270–272] using electrocorticography (ECoG)[273,274] on the cortical surface as well as local field potentials (LFP)[275,276] extracted from electrodes inserted into the cortex.”
From Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics (2022) · cites this paper as [270]
The cited source says what the claim attributes to it.
In the citing paper
“The signal recorded by EEG mainly reflects synchronised postsynaptic activity generated by large populations of cortical pyramidal neurons [17].”
In the citing paper
“As expected, the numerically dominant pyramidal cells, whose intracellular membrane potential oscillations to a large extent generate and underlie the extracellular LFP signal during theta oscillations (Buzsáki et al., 2012), preferentially discharged around the trough 0o/360o of the LFP analog theta rhythm (Figure 5A).”
From Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit (2016) · cites this paper as Buzsáki et al., 2012
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576
Buzsáki, G. (2006) · doi:10.1093/acprof:oso/9780195301069.001.0001 · Cited by 4,873
Self-citationHämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.413 · Cited by 4,684
Thalamocortical oscillations in the sleeping and aroused brain
Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993) · Science · doi:10.1126/science.8235588 · Cited by 3,564
Interneurons of the hippocampus
Freund, T. F., & Buzsáki, G. (1998) · Hippocampus · doi:10.1002/(sici)1098-1063(1996)6:4 · Cited by 3,478
Self-citationElectroencephalography—Basic principles, clinical applications and related fields
(1982) · Journal of the Autonomic Nervous System · doi:10.1016/0165-1838(82)90067-4 · Cited by 3,449
Theta oscillations in the hippocampus
Buzsáki, G. (2002) · Neuron · doi:10.1016/s0896-6273(02)00586-x · Cited by 3,332
Self-citationHigh Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., Berger, M. S., Barbaro, N. M., & Knight, R. T. (2006) · Science · doi:10.1126/science.1128115 · Cited by 2,650
Nunez, P. L., & Srinivasan, R. (2006) · doi:10.1093/acprof:oso/9780195050387.001.0001 · Cited by 2,531
Flagged first, then by citations
1 of the 162 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Buzsáki, G., Traub, R. D. & Pedley, T. A. in Current Practice of Clinical Encephalography (eds Ebersole, J. S. & Pedley, T. A.) 1–11 (Lippincott-Williams and Wilkins, 2003).
- Stuart, G., Spruston, N. & Hausser, M. Dendrites (Oxford Univ. Press, 2008).
- Steriade, M. & Buzsáki, G. in Brain Cholinergic System (eds Steriade, M. & Biesold, D.) 3–64 (Oxford Univ. Press, 1989).
- He, B. J., Snyder, A. Z., Zempel, J. M., Smyth, M. D. & Raichle, M. E. Electrophysiological correlates of the brain's intrinsic large-scale functional architecture. Proc. Natl Acad. Sci. USA 105, 16039–16044 (2008). · DOI · Paper page
- Alifanov, O. M. Solution of the inverse heat conduction problems by iterative methods. J. Eng. Physics (Russia) 26, 682–689 (1974).
Cited by
Sorted by citations
Parameterizing neural power spectra into periodic and aperiodic components
Donoghue, T., Haller, M., Peterson, E. J., Varma, P., Sebastian, P., Gao, R., Noto, T., Lara, A. H., Wallis, J. D., Knight, R. T., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00744-x · Cited by 2,417
Yuk, H., Lu, B., & Zhao, X. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00595h · Cited by 2,148
Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning
Buzsáki, G. (2015) · Hippocampus · doi:10.1002/hipo.22488 · Cited by 1,759
Self-citationLisman, J. E., & Jensen, O. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.007 · Cited by 1,609
Deep learning in bioinformatics
Min, S., Lee, B., & Yoon, S. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw068 · Cited by 1,571
Closed-loop brain training: the science of neurofeedback
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M. L., Rana, M., Oblak, E., et al. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.164 · Cited by 1,306
The steady-state visual evoked potential in vision research: A review
Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015) · Journal of Vision · doi:10.1167/15.6.4 · Cited by 1,234
An Integrated Brain-Machine Interface Platform With Thousands of Channels
Musk, E., & Neuralink (2019) · Journal of Medical Internet Research · doi:10.2196/16194 · Cited by 1,206
In vivo recordings of brain activity using organic transistors
Khodagholy, D., Doublet, T., Quilichini, P., Gurfinkel, M., Leleux, P., Ghestem, A., Ismailova, E., Hervé, T., Sanaur, S., Bernard, C., et al. (2013) · Nature Communications · doi:10.1038/ncomms2573 · Cited by 1,057
Inferring synaptic excitation/inhibition balance from field potentials
Gao, R., Peterson, E. J., & Voytek, B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.078 · Cited by 1,069
Magnetoencephalography for Brain Electrophysiology and Imaging
Baillet, S. (2017) · Nature Neuroscience · doi:10.1038/nn.4504 · Cited by 992
Multi-electrode array technologies for neuroscience and cardiology
Spira, M. E., & Hai, A. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.265 · Cited by 991
Trujillo, C. A., Gao, R., Negraes, P. D., Gu, J., Buchanan, J., Preissl, S., Wang, A., Wu, W., Haddad, G. G., Chaim, I. A., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.08.002 · Cited by 974
The log-dynamic brain: how skewed distributions affect network operations
Buzsáki, G., & Mizuseki, K. (2014) · Nature reviews. Neuroscience · doi:10.1038/nrn3687 · Cited by 1,019
Self-citationvan Kerkoerle, T., Self, M. W., Dagnino, B., Gariel-Mathis, M.-A., Poort, J., van der Togt, C., & Roelfsema, P. R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402773111 · Cited by 918
EEG and MEG: Relevance to Neuroscience
Lopes da Silva, F. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.017 · Cited by 900
Clinical Electroencephalography for Anesthesiologists
Purdon, P. L., Sampson, A. L., Pavone, K. J., & Brown, E. N. (2015) · Anesthesiology · doi:10.1097/aln.0000000000000841 · Cited by 889
Modelling and analysis of local field potentials for studying the function of cortical circuits
Einevoll, G. T., Kayser, C., Logothetis, N. K., & Panzeri, S. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3599 · Cited by 857
Diaschisis: past, present, future
Carrera, E., & Tononi, G. (2014) · Brain · doi:10.1093/brain/awu101 · Cited by 860
The functional significance of delta oscillations in cognitive processing
Harmony, T. (2013) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2013.00083 · Cited by 788
Where Does EEG Come From and What Does It Mean?
Cohen, M. X. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.02.004 · Cited by 722
Revealing neuronal function through microelectrode array recordings
Obien, M. E. J., Deligkaris, K., Bullmann, T., Bakkum, D. J., & Frey, U. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00423 · Cited by 705
High-Frequency Brain Activity and Muscle Artifacts in MEG/EEG: A Review and Recommendations
Muthukumaraswamy, S. D. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00138 · Cited by 697
Novel electrode technologies for neural recordings
Hong, G., & Lieber, C. M. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0140-6 · Cited by 695
Zhang, A., & Lieber, C. M. (2015) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00608 · Cited by 642
Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics
Heng, W., Solomon, S., & Gao, W. (2022) · Advanced Materials · doi:10.1002/adma.202107902 · Cited by 609
Cortical travelling waves: mechanisms and computational principles
Muller, L., Chavane, F., Reynolds, J. H., & B, T. J. S. A. (2018) · Nature reviews. Neuroscience · doi:10.1038/nrn.2018.20 · Cited by 617
Immediate neurophysiological effects of transcranial electrical stimulation
Liu, A. A., Vöröslakos, M., Kronberg, G., Henin, S. E., Krause, M. R., Huang, Y., Opitz, A., Mehta, A. D., Pack, C. C., Krekelberg, B., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07233-7 · Cited by 604
Self-citationPrefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior
Ferenczi, E. A., Zalocusky, K. A., Liston, C., Grosenick, L., Warden, M. R., Amatya, D., Katovich, K., Mehta, H., Patenaude, B., Ramakrishnan, C., et al. (2016) · Science · doi:10.1126/science.aac9698 · Cited by 535
Next-generation probes, particles, and proteins for neural interfacing
Rivnay, J., Wang, H., Fenno, L., Deisseroth, K., & Malliaras, G. G. (2017) · Science Advances · doi:10.1126/sciadv.1601649 · Cited by 514
Alterations in Cortical Network Oscillations and Parvalbumin Neurons in Schizophrenia
Gonzalez-Burgos, G., Cho, R. Y., & Lewis, D. A. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.03.010 · Cited by 497
Sherman, M. A., Lee, S., Law, R., Haegens, S., Thorn, C. A., Hämäläinen, M., Moore, C. I., & Jones, S. R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604135113 · Cited by 471
The Oxford Handbook of Perceptual Organization
Lier, R. J. V., Gerbino, W., & Wagemans, J. (2015) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199686858.001.0001 · Cited by 469
Theta Phase Segregation of Input-Specific Gamma Patterns in Entorhinal-Hippocampal Networks
Schomburg, E. W., Fernández-Ruiz, A., Mizuseki, K., Berényi, A., Anastassiou, C. A., Koch, C., & Buzsáki, G. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.051 · Cited by 465
Self-citationPadilla-Coreano, N., Bolkan, S. S., Pierce, G. M., Blackman, D. R., Hardin, W. D., Garcia-Garcia, A. L., Spellman, T. J., & Gordon, J. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.01.011 · Cited by 463
Zrenner, C., Desideri, D., Belardinelli, P., & Ziemann, U. (2017) · Brain stimulation · doi:10.1016/j.brs.2017.11.016 · Cited by 462
Past, present and future of spike sorting techniques
Rey, H. G., Pedreira, C., & Quiroga, R. Q. (2015) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2015.04.007 · Cited by 451
The functional role of beta oscillations in Parkinson's disease
Little, S. J., & Brown, P. (2013) · Parkinsonism & Related Disorders · doi:10.1016/s1353-8020(13)70013-0 · Cited by 422
Neural Entrainment and Attentional Selection in the Listening Brain
Obleser, J., & Kayser, C. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.08.004 · Cited by 421
Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal
Wen, H., & Liu, Z. (2016) · Brain Topography · doi:10.1007/s10548-015-0448-0 · Cited by 419
The generation and propagation of the human alpha rhythm
Halgren, M., Ulbert, I., Bastuji, H., Fabó, D., Erőss, L., Rey, M. F., Devinsky, O., Doyle, W., Mak-McCully, R. A., Halgren, E., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1913092116 · Cited by 418
Nagai, Y., Miyakawa, N., Takuwa, H., Hori, Y., Oyama, K., Ji, B., Takahashi, M., Huang, X., Slocum, S. T., DiBerto, J. F., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0661-3 · Cited by 438
An electrophysiological marker of arousal level in humans
Lendner, J. D., Helfrich, R. F., Mander, B. A., Romundstad, L. G., Lin, J. J., Walker, M. P., Larsson, P. G., & Knight, R. T. (2020) · eLife · doi:10.7554/elife.55092 · Cited by 425
Finding the beat: a neural perspective across humans and non-human primates
Merchant, H., Grahn, J. A., Trainor, L. J., Rohrmeier, M., & Fitch, W. T. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0093 · Cited by 410
Serotonergic neurons signal reward and punishment on multiple timescales
Cohen, J. Y., Amoroso, M. W., & Uchida, N. (2015) · eLife · doi:10.7554/elife.06346 · Cited by 388
Oscillations and the basal ganglia: Motor control and beyond
Brittain, J., & Brown, P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.084 · Cited by 381
Alekseichuk, I., Turi, Z., de Lara, G., Antal, A., & Paulus, W. (2016) · Current Biology · doi:10.1016/j.cub.2016.04.035 · Cited by 389
Restoring sensorimotor function through intracortical interfaces: progress and looming challenges
Bensmaia, S. J., & Miller, L. E. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3724 · Cited by 368
Pesaran, B., Vinck, M. A., Einevoll, G. T., Sirota, A., Fries, P., Siegel, M., Truccolo, W., Schroeder, C. E., & Srinivasan, R. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0171-8 · Cited by 378
Oscillations and Episodic Memory: Addressing the Synchronization/Desynchronization Conundrum
Hanslmayr, S., Staresina, B. P., & Bowman, H. (2016) · Trends in Neurosciences · doi:10.1016/j.tins.2015.11.004 · Cited by 394
Sleep and the single neuron: the role of global slow oscillations in individual cell rest
Vyazovskiy, V. V., & Harris, K. D. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3494 · Cited by 386
Buzsáki, G. (2019) · doi:10.1093/oso/9780190905385.001.0001 · Cited by 383
Self-citationThe organic electrochemical transistor for biological applications
Strakosas, X., Bongo, M., & Owens, R. M. (2015) · Journal of Applied Polymer Science · doi:10.1002/app.41735 · Cited by 356
Nanotools for Neuroscience and Brain Activity Mapping
Alivisatos, A. P., Andrews, A. M., Boyden, E. S., Chun, M., Church, G. M., Deisseroth, K. A., Donoghue, J. P., Fraser, S. E., Lippincott‐Schwartz, J., Looger, L. L., et al. (2013) · ACS Nano · doi:10.1021/nn4012847 · Cited by 359
Entorhinal-CA3 Dual-Input Control of Spike Timing in the Hippocampus by Theta-Gamma Coupling
Fernández‐Ruiz, A., Oliva, A., Nagy, G. A., Maurer, A. P., Berényi, A., & Buzsáki, G. (2017) · Neuron · doi:10.1016/j.neuron.2017.02.017 · Cited by 344
Self-citationCausal mapping of human brain function
Siddiqi, S., Kording, K. P., Parvizi, J., & Fox, M. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00583-8 · Cited by 368
Local Field Potentials: Myths and Misunderstandings
Herreras, Ó. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00101 · Cited by 364
Tools for Probing Local Circuits: High-Density Silicon Probes Combined with Optogenetics
Buzsáki, G., Stark, E., Berényi, A., Khodagholy, D., Kipke, D. R., Yoon, E., & Wise, K. D. (2015) · Neuron · doi:10.1016/j.neuron.2015.01.028 · Cited by 337
Self-citationOscillatory multiplexing of population codes for selective communication in the mammalian brain
Akam, T., & Kullmann, D. M. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3668 · Cited by 327
Hashemi, E., Ariza, J., Rogers, H., Noctor, S. C., & Martínez-Cerdeño, V. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw021 · Cited by 322
Neural correlates of the DMT experience assessed with multivariate EEG
Timmermann, C., Roseman, L., Schartner, M., Milliere, R., Williams, L. T. J., Erritzoe, D., Muthukumaraswamy, S., Ashton, M., Bendrioua, A., Kaur, O., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-51974-4 · Cited by 315
What does gamma coherence tell us about inter-regional neural communication?
Buzsáki, G., & Schomburg, E. W. (2015) · Nature Neuroscience · doi:10.1038/nn.3952 · Cited by 333
Self-citationNeural Networks for Beat Perception in Musical Rhythm
Large, E. W., Herrera, J., & Velasco, M. J. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00159 · Cited by 339
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
Hippocampal-Prefrontal Interactions in Cognition, Behavior and Psychiatric Disease
Sigurdsson, T., & Duvarci, S. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00190 · Cited by 309
Interface, interaction, and intelligence in generalized brain–computer interfaces
Gao, X., Wang, Y., Chen, X., & Gao, S. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.04.003 · Cited by 324
Layer-specific modulation of neocortical dendritic inhibition during active wakefulness
Muñoz, W., Tremblay, R., Levenstein, D., & Rudy, B. (2017) · Science · doi:10.1126/science.aag2599 · Cited by 305
Advancing functional connectivity research from association to causation
Reid, A., Headley, D. B., Mill, R. D., Sánchez-Romero, R., Uddin, L. Q., Marinazzo, D., Lurie, D. J., Valdes-Sosa, P. A., Hanson, S. J., Biswal, B. B., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0510-4 · Cited by 303
Illuminating dendritic function with computational models
Poirazi, P., & Papoutsi, A. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-0301-7 · Cited by 313
Behavior needs neural variability
Waschke, L., Kloosterman, N. A., Obleser, J., & Garrett, D. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.023 · Cited by 317
Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals
Berényi, A., Somogyvári, Z., Nagy, A. J., Roux, L., Long, J. D., Fujisawa, S., Stark, E., Leonardo, A. M., Harris, T. D., & Buzsáki, G. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00785.2013 · Cited by 305
Self-citationCea, C., Spyropoulos, G. D., Jastrzebska‐Perfect, P., Ferrero, J. J., Gelinas, J. N., & Khodagholy, D. (2020) · Nature Materials · doi:10.1038/s41563-020-0638-3 · Cited by 294
Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture
Gao, R., van den Brink, R. L., Pfeffer, T. A., & Voytek, B. (2020) · eLife · doi:10.7554/elife.61277 · Cited by 320
Frontosubthalamic Circuits for Control of Action and Cognition
Aron, A. R., Herz, D. M., Brown, P., Forstmann, B. U., & Zaghloul, K. A. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2348-16.2016 · Cited by 299
Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex
Paulk, A. C., Kfir, Y., Khanna, A. R., Mustroph, M. L., Trautmann, E. M., Soper, D. J., Stavisky, S. D., Welkenhuysen, M., Dutta, B., Shenoy, K. V., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00997-0 · Cited by 283
van Kerkoerle, T., Self, M. W., & Roelfsema, P. R. (2017) · Nature Communications · doi:10.1038/ncomms13804 · Cited by 281
Reimann, M., Anastassiou, C. A., Perin, R., Hill, S., Markram, H., & Koch, C. (2013) · Neuron · doi:10.1016/j.neuron.2013.05.023 · Cited by 279
Self-citationThe Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging
Ritter, P., Schirner, M., McIntosh, A. R., & Jirsa, V. K. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0120 · Cited by 268
The neurophysiological bases of EEG and EEG measurement: A review for the rest of us
Jackson, A. F., & Bolger, D. J. (2014) · Psychophysiology · doi:10.1111/psyp.12283 · Cited by 267
Critical slowing down as a biomarker for seizure susceptibility
Maturana, M. I., Meisel, C., Dell, K. L., Karoly, P. J., D'Souza, W. J., Grayden, D. B., Burkitt, A. N., Jiruška, P., Kudláček, J., Hlinka, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15908-3 · Cited by 282
Physical Principles for Scalable Neural Recording
Marblestone, A. H., Zamft*, B. M., Maguire, Y. G., Shapiro, M. G., Cybulski, T. R., Glaser, J. I., eAmodei, D., eStranges, P. B., eKalhor, R., Dalrymple, D., et al. (2013) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.3389/fncom.2013.00137 · Cited by 271
Fontolan, L., Morillon, B., Liégeois‐Chauvel, C., & Giraud, A. (2014) · Nature Communications · doi:10.1038/ncomms5694 · Cited by 286
Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework
Ros, T., Baars, B. J., Lanius, R. A., & Vuilleumier, P. O. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.01008 · Cited by 283
Thut, G., Bergmann, T. O., Frӧhlich, F., Soekadar, S. R., Brittain, J., Valero‐Cabré, A., Sack, A. T., Miniussi, C., Antal, A., Siebner, H. R., et al. (2017) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.01.003 · Cited by 280
Senzai, Y., Fernández‐Ruiz, A., & Buzsáki, G. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.009 · Cited by 274
Self-citationNeuroinflammation, Sleep, and Circadian Rhythms
Zielinski, M. R., & Gibbons, A. J. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.853096 · Cited by 273
Do gamma oscillations play a role in cerebral cortex?
Ray, S., & Maunsell, J. H. R. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.12.002 · Cited by 272
Polanía, R., Krajbich, I. M., Grueschow, M., & Ruff, C. C. (2014) · Neuron · doi:10.1016/j.neuron.2014.03.014 · Cited by 285
Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations
Gerster, M., Waterstraat, G., Vladimir Litvak, A., Lehnertz, K., Schnitzler, A., Florin, E., Curio, G., & Nikulin, V. V. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09581-8 · Cited by 275
Bocchio, M., Nabavi, S., & Capogna, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.022 · Cited by 265
Tissue adhesive hydrogel bioelectronics
Li, S., Cong, Y., & Fu, J. (2021) · Journal of Materials Chemistry B · doi:10.1039/d1tb00523e · Cited by 261
An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing
Sarkar, T., Lieberth, K., Pavlou, A., Frank, T., Mailänder, V., McCulloch, I. A., Blom, P. W. M., Torricelli, F., & Gkoupidenis, P. (2022) · Nature Electronics · doi:10.1038/s41928-022-00859-y · Cited by 261
Alia, C., Spalletti, C., Lai, S., Panarese, A., Lamola, G., Bertolucci, F., Vallone, F., Garbo, A. D., Chisari, C., Micera, S., et al. (2017) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2017.00076 · Cited by 272
Prefrontal Cortex Development in Health and Disease: Lessons from Rodents and Humans
Chini, M., & Hanganu-Opatz, I. L. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2020.10.017 · Cited by 253
Alterations in neuronal activity in basal ganglia-thalamocortical circuits in the parkinsonian state
Galván, A., Devergnas, A. D., & Wichmann, T. (2015) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2015.00005 · Cited by 264
On-skin paintable biogel for long-term high-fidelity electroencephalogram recording
Wang, C., Wang, H., Wang, B., Miyata, H., Wang, Y., Nayeem, M. O. G., Kim, J. J., Lee, S., Yokota, T., Onodera, H., et al. (2022) · Science Advances · doi:10.1126/sciadv.abo1396 · Cited by 247
Waschke, L., Donoghue, T., Fiedler, L., Smith, S. E., Garrett, D. D., Voytek, B., & Obleser, J. (2021) · eLife · doi:10.7554/elife.70068 · Cited by 251
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
Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review
Li, R., Yang, D., Fang, F. Z., Hong, K., Reiss, A. L., & Zhang, Y. (2022) · Sensors · doi:10.3390/s22155865 · Cited by 244
Functional neuronal circuitry and oscillatory dynamics in human brain organoids
Sharf, T., van der Molen, T., Glasauer, S. M. K., Guzman, E., Buccino, A. P., Luna, G., Cheng, Z., Audouard, M., Ranasinghe, K. G., Kudo, K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32115-4 · Cited by 251
Metadata
- Authors
- Buzsáki, G., Anastassiou, C. A., & Koch, C.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- California Institute of Technology (US)
- Corresponding author
- Buzsáki, György
- Venue
- Nature Reviews Neuroscience
- Published
- 2012-05-18
- Volume
- 13
- Issue
- 6
- Pages
- 407-420
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- PMC
- PMC4907333
- OpenAlex
- W1973275441
- Cited by
- 4,225
- References
- 162
- Citations by year
- 2026: 299 · 2025: 395 · 2024: 340 · 2023: 413 · 2022: 406 · 2021: 354 · 2020: 343 · 2019: 308 · 2018: 291 · 2017: 273 · 2016: 249 · 2015: 205 · 2014: 193 · 2013: 124 · 2012: 29
- FWCI
- 13.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (131470)
- Hebrew University of Jerusalem
- Universität Konstanz
- National Institutes of Health
- National Institute of Mental Health (R01MH054671, MH54671)
- National Institute of Neurological Disorders and Stroke (NS074015, R01NS034994, R01NS074015, NS-34994)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- EEG, electrocorticography, local field potentials, neuronal spikes, membrane currents, synaptic currents, action potential, voltage-gated ion channels, ligand-gated ion channels, membrane potential oscillations, high-density recording, neuronal synchronization
- MeSH terms
- Animals, Electroencephalography, Evoked Potentials, Extracellular Space, Humans, Neural Conduction, Neuroglia, Neurons, Synapses, Magnetoencephalography, Calcium Signaling, Electrical Synapses, Voltage-Sensitive Dye Imaging, Ligand-Gated Ion Channels
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
