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Fully integrated silicon probes for high-density recording of neural activity
Open accessAbstract
Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca 2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal-oxide-semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
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“However, these normative models 29 (i.e., theories for why remapping occurs) do not address how a biological system might implement this strategy.”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [29]
In the citing paper
“Current state-of-the-art silicon-based in vivo systems do not commonly feature more than hundreds of electrodes (Campbell et al. 1991), with recent improvements bringing this number closer to one thousand (Shobe et al. 2015; Scholvin et al. 2016; Buszáki et al. 2015), even on a single probe (Jun et al. 2017).”
From Realizing flexible bioelectronic medicines for accessing the peripheral nerves – technology considerations (2018) · cites this paper as Jun et al. 2017
In the citing paper
“In addition, it has a user-programmable switch that allows us to address 384 of the 960 sites simultaneously.”
From Advances in Neural Recording and Stimulation Integrated Circuits (2021) · cites this paper as Jun et al., 2017
In the citing paper
“The probe is 10 mm long and contains 960 sites.”
From Advances in Neural Recording and Stimulation Integrated Circuits (2021) · cites this paper as Jun et al., 2017
In the citing paper
“This is the first report of a large (10 mm) dense (100 sites per millimeter) implantable nerve recording device.”
From Advances in Neural Recording and Stimulation Integrated Circuits (2021) · cites this paper as Jun et al., 2017
In the citing paper
“Titanium carbide (TIN) is selected as the recording site material, which is compatible with CMOS processing and has the advantages of low and uniform impedance.”
From Advances in Neural Recording and Stimulation Integrated Circuits (2021) · cites this paper as Jun et al., 2017
In the citing paper
“The researchers used two Neuropixels probes to record the activities of more than 700 neurons.”
From Advances in Neural Recording and Stimulation Integrated Circuits (2021) · cites this paper as Jun et al., 2017
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Cites
Microstructure of a spatial map in the entorhinal cortex
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., & Moser, E. I. (2005) · Nature · doi:10.1038/nature03721 · Cited by 4,068
Neurodegenerative Disorders: Parkinson's Disease and Huntington's Disease
Hague, S., Klaffke, S., & Bandmann, O. (2006) · IMPERIAL COLLEGE PRESS eBooks · doi:10.1142/9781860948961_0007 · Cited by 2,767
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
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
Optogenetics in neural systems
Yizhar, O., Fenno, L. E., Davidson, T. J., Mogri, M., & Deisseroth, K. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.004 · Cited by 1,881
Large-scale recording of neuronal ensembles
Buzsáki, G. (2004) · Nature Neuroscience · doi:10.1038/nn1233 · Cited by 1,875
Whole-brain functional imaging at cellular resolution using light-sheet microscopy
Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M., & Keller, P. J. (2013) · Nature Methods · doi:10.1038/nmeth.2434 · Cited by 1,392
High-frequency network oscillation in the hippocampus
Buzsáki, G., Horváth, Z., Urioste, R., Hetke, J. F., & Wise, K. D. (1992) · Science · doi:10.1126/science.1589772 · Cited by 1,223
Harris, K. D., Henze, D. A., Csicsvari, J. L., Hirase, H., & Buzsáki, G. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.1.401 · Cited by 1,153
Self-citationSynchronous bursts of action potentials in ganglion cells of the developing mammalian retina
Meister, M., Wong, R. O. L., Baylor, D. A., & Shatz, C. J. (1991) · Science · doi:10.1126/science.2035024 · Cited by 1,110
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- Musa, S. Titanium nitride electrode. US patent 9,384,990 (2016)
Cited by
Sorted by citations
Items 401–500 of 1,004
Bergeron, D., Iorio‐Morin, C., Bonizzato, M., Lajoie, G., Gaucher, N. O., Racine, É., & Weil, A. G. (2023) · Journal of Child Neurology · doi:10.1177/08830738231167736 · Cited by 50
Song, E., Li, J., & Rogers, J. A. (2019) · APL Materials · doi:10.1063/1.5094415 · Cited by 49
Nanoenabled Direct Contact Interfacing of Syringe-Injectable Mesh Electronics
Lee, J. M., Hong, G., Lin, D., Schuhmann, T. G., Sullivan, A. T., Viveros, R. D., Park, H., & Lieber, C. M. (2019) · Nano Letters · doi:10.1021/acs.nanolett.9b03019 · Cited by 48
Agmon, H., & Burak, Y. (2020) · eLife · doi:10.7554/elife.56894 · Cited by 50
Single-neuron firing cascades underlie global spontaneous brain events
Liu, X., Leopold, D. A., & Yang, Y. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105395118 · Cited by 53
Suzuki, I., Matsuda, N., Han, X. B., Noji, S., Shibata, M., Nagafuku, N., & Ishibashi, Y. (2023) · Advanced Science · doi:10.1002/advs.202207732 · Cited by 48
Self-assembled ultraflexible probes for long-term neural recordings and neuromodulation
Guan, S., Tian, H., Yang, Y., Liu, M., Ding, J., Wang, J., & Fang, Y. Z. (2023) · Nature Protocols · doi:10.1038/s41596-023-00824-9 · Cited by 48
Caillet, A. H., Avrillon, S., Kundu, A., Yu, T., Phillips, A., Modenese, L., & Farina, D. (2023) · eNeuro · doi:10.1523/eneuro.0064-23.2023 · Cited by 53
Intermittent rate coding and cue-specific ensembles support working memory
Panichello, M. F., Jonikaitis, D., Oh, Y. J., Zhu, S., Trepka, E. B., & Moore, T. (2024) · Nature · doi:10.1038/s41586-024-08139-9 · Cited by 52
Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque
Bollimunta, A., Santacruz, S. R., Eaton, R. W., Xu, P. S., Morrison, J. H., Moxon, K. A., Carmena, J. M., & Nassi, J. J. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109239 · Cited by 48
Impedance Imaging of Cells and Tissues: Design and Applications
Bounik, R., Cardes, F., Uluşan, H., Modena, M. M., & Hierlemann, A. R. (2022) · BME Frontiers · doi:10.34133/2022/9857485 · Cited by 47
Li, P., Garg, A., Zhang, L. A., Rashid, M., & Callaway, E. M. (2022) · Nature Communications · doi:10.1038/s41467-022-34020-2 · Cited by 51
Sleep/wake changes in perturbational complexity in rats and mice
Cavelli, M. L., Mao, R., Findlay, G., Driessen, K., Bugnon, T., Tononi, G., & Cirelli, C. (2023) · iScience · doi:10.1016/j.isci.2023.106186 · Cited by 46
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
Modified Neuropixels probes for recording human neurophysiology in the operating room
Coughlin, B. F., Muñoz, W., Kfir, Y., Young, M. J., Meszéna, D., Jamali, M., Caprara, I., Hardstone, R., Khanna, A. R., Mustroph, M. L., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00871-2 · Cited by 47
Self-citationOne-shot entorhinal maps enable flexible navigation in novel environments
Wen, J. H., Sorscher, B., Jones, E. A. A., Ganguli, S., & Giocomo, L. M. (2024) · Nature · doi:10.1038/s41586-024-08034-3 · Cited by 49
Navigating Through Time: A Spatial Navigation Perspective on How the Brain May Encode Time
Issa, J. B., Tocker, G., Hasselmo, M. E., Heys, J. G., & Dombeck, D. A. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-101419-011117 · Cited by 49
Ling, W., Yu, J., Ma, N., Ya, L., Wu, Z., Liang, R., Hao, Y., Pan, H., Liu, W., Fu, B., et al. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202002644 · Cited by 47
Behavioral decomposition reveals rich encoding structure employed across neocortex in rats
Mimica, B., Tombaz, T., Battistin, C., Fuglstad, J. G., Dunn, B. A., & Whitlock, J. R. (2023) · Nature Communications · doi:10.1038/s41467-023-39520-3 · Cited by 45
Shin, H., Byun, J., Roh, D., Choi, N., Shin, H., & Cho, I. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113665 · Cited by 45
Blazed oblique plane microscopy reveals scale-invariant inference of brain-wide population activity
Hoffmann, M., Henninger, J., Veith, J., Richter, L., & Judkewitz, B. (2023) · Nature Communications · doi:10.1038/s41467-023-43741-x · Cited by 48
Tissue-like Neural Probes for Understanding and Modulating the Brain
Hong, G., Viveros, R. D., Zwang, T. J., Yang, X., & Lieber, C. M. (2018) · Biochemistry · doi:10.1021/acs.biochem.8b00122 · Cited by 46
Multisite Attenuated Intracellular Recordings by Extracellular Multielectrode Arrays, a Perspective
Spira, M. E., Shmoel, N., Huang, S.-H., & Erez, H. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00212 · Cited by 45
Modular organization of cerebellar climbing fiber inputs during goal-directed behavior
Tsutsumi, S., Hidaka, N., Isomura, Y., Matsuzaki, M., Sakimura, K., Kano, M., & Kitamura, K. (2019) · eLife · doi:10.7554/elife.47021 · Cited by 45
Growing evidence for separate neural mechanisms for attention and consciousness
Maier, A., & Tsuchiya, N. (2020) · Attention Perception & Psychophysics · doi:10.3758/s13414-020-02146-4 · Cited by 47
Microscale Physiological Events on the Human Cortical Surface
Paulk, A. C., Yang, J. C., Cleary, D. R., Soper, D. J., Halgren, M., O’Donnell, A. R., Lee, S., Ganji, M., Ro, Y. G., Oh, H., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab040 · Cited by 47
Flexible, high-resolution thin-film electrodes for human and animal neural research
Chiang, C., Wang, C., Barth, K. J., Rahimpour, S., Trumpis, M., Duraivel, S., Rachinskiy, I., Dubey, A., Wingel, K., Wong, M. K., et al. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac02dc · Cited by 47
Next-generation of recurrent neural network models for cognitionPreprint
Yang, G. R., & Molano‐Mazón, M. (2021) · PsyArXiv (OSF Preprints) · doi:10.1016/j.conb.2021.10.015 · Cited by 49
Interhemispheric competition during sleep
Fenk, L. A., Riquelme, J. L., & Laurent, G. (2023) · Nature · doi:10.1038/s41586-023-05827-w · Cited by 46
Developing Next-Generation Brain Sensing Technologies—A Review
Robinson, J. T., Xie, C., Pohlmeyer, E. A., Gather, M. C., Kemere, C. T., Kitching, J. E., Malliaras, G. G., Marblestone, A. H., Shepard, K. L., & Stieglitz, T. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2931159 · Cited by 45
An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks
Li, Z., Wang, Y., Zhang, N., & Li, X. (2020) · Brain Sciences · doi:10.3390/brainsci10110835 · Cited by 47
Ceramic packaging in neural implants
Shen, K., & Maharbiz, M. M. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abd683 · Cited by 47
Sharing neurophysiology data from the Allen Brain Observatory
de Vries, S. E. J., Siegle, J. H., & Koch, C. (2023) · eLife · doi:10.7554/elife.85550 · Cited by 45
Self-citationAdvances in large-scale electrophysiology with high-density microelectrode arrays
Schröter, M. S., Cardes, F., Bui, C.-V. H., Dodi, L. D., Gänswein, T., Bartram, J., Sadiraj, L., Hornauer, P., Kumar, S. S., Pascual-Garcia, M., et al. (2025) · Lab on a Chip · doi:10.1039/d5lc00058k · Cited by 48
State-space optimal feedback control of optogenetically driven neural activity
Bolus, M. F., Willats, A. A., Rozell, C. J., & Stanley, G. B. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abb89c · Cited by 43
Whole-brain interactions underlying zebrafish behavior
Loring, M. D., Thomson, E. E., & Naumann, E. A. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.09.011 · Cited by 41
Rodent somatosensory thalamocortical circuitry: Neurons, synapses, and connectivity
O’Reilly, C., Iavarone, E., Yi, J., & Hill, S. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.03.015 · Cited by 43
Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System
Sorrell, E., Rule, M. E., & O’Leary, T. (2021) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-061720-012348 · Cited by 41
Chiappalone, M., Cota, V. R., Carè, M., Florio, M. D., Beaubois, R., Buccelli, S., Barban, F., Brofiga, M., Averna, A., Bonacini, F., et al. (2022) · Brain Sciences · doi:10.3390/brainsci12111578 · Cited by 45
Song, M., Huang, Y. T., Visser, H. J., Romme, J., & Liu, Y. (2022) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2022.3212672 · Cited by 45
Organoid intelligence (OI) – The ultimate functionality of a brain microphysiological system
Smirnova, L. (2023) · ALTEX · doi:10.14573/altex.2303261 · Cited by 45
Neurotechnologies to restore hand functions
Losanno, E., Mender, M. J., Chestek, C. A., Shokur, S., & Micera, S. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00054-4 · Cited by 45
A neural mechanism for learning from delayed postingestive feedback
Zimmerman, C. A., Bolkan, S. S., Pan-Vazquez, A., Wu, B., Keppler, E., Meares-Garcia, J. B., Guthman, E. M., Fetcho, R. N., McMannon, B., Lee, J., et al. (2025) · Nature · doi:10.1038/s41586-025-08828-z · Cited by 49
Multi-scale neural decoding and analysis
Lu, H., Lorenc, E. S., Zhu, H., Kilmarx, J., Sulzer, J. S., Xie, C., Tobler, P. N., Watrous, A. J., Orsborn, A. L., Lewis‐Peacock, J. A., et al. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac160f · Cited by 47
Neurotechnological Approaches to the Diagnosis and Treatment of Alzheimer’s Disease
Ning, S., Jorfi, M., Patel, S. R., Kim, D. Y., & Tanzi, R. E. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.854992 · Cited by 43
Implantable Micro-Light-Emitting Diode (µLED)-based optogenetic interfaces toward human applications
Lee, J. H., Lee, S., Kim, D., & Lee, K. J. (2022) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2022.114399 · Cited by 43
Navas-Olivé, A., Amaducci, R., Jurado-Parras, M.-T., Sebastian, E. R., & de la Prida, L. M. (2022) · eLife · doi:10.7554/elife.77772 · Cited by 43
Advanced Nanomaterials in Medical 3D Printing
Liu, H., He, L., Kuzmanović, M., Huang, Y., Zhang, L., Zhang, Y., Zhu, Q., Ren, Y., Dong, Y., Cardon, L., et al. (2023) · Small Methods · doi:10.1002/smtd.202301121 · Cited by 41
Long-lasting forms of plasticity through patterned ultrasound-induced brainwave entrainment
Kim, H.-J., Phan, T. T., Lee, K., Kim, J., Lee, S.-Y., Lee, J. M., Do, J., Lee, D., Kim, S.-P., Lee, K. P., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk3198 · Cited by 41
Milioto, C., Carcolé, M., Giblin, A., Coneys, R., Attrebi, O. N., Ahmed, M., Harris, S., Lee, B. I., Yang, M., Ellingford, R. A., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01589-4 · Cited by 46
Brain-wide analysis reveals movement encoding structured across and within brain areas
Wang, Z. A., Kurgyis, B., Chen, S., Kang, B., Chen, F., Liu, Y., Liu, D., Svoboda, K., Li, N., & Druckmann, S. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02114-x · Cited by 46
Self-citationProgress in Neuroengineering for brain repair: New challenges and open issues
Panuccio, G., Semprini, M., Natale, L., Buccelli, S., Colombi, I., & Chiappalone, M. (2018) · Brain and Neuroscience Advances · doi:10.1177/2398212818776475 · Cited by 44
Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs
Zhang, Z., & Constandinou, T. G. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109103 · Cited by 41
Pimenta, S., Rodrigues, J. A., Machado, F., Ribeiro, J. F., Maciel, M. J., Bondarchuk, O. B., Monteiro, P., Gaspar, J., Correia, J. H., & Jacinto, L. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.663174 · Cited by 44
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural PopulationsPreprint
Glaser, J. I., Whiteway, M. R., Cunningham, J. P., Paninski, L., & Linderman, S. W. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.21.349282 · Cited by 41
Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey
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
Roadmap on low-power electronics
Ramesh, R., Salahuddin, S., Datta, S., Diaz, C. H., Nikonov, D. E., Young, I. A., Ham, D., Chang, M., Khwa, W.-S., Lele, A. S., et al. (2024) · APL Materials · doi:10.1063/5.0184774 · Cited by 43
Flexible Microinterventional Sensors for Advanced Biosignal Monitoring
Liu, Y., Fan, P., Pan, Y., & Ping, J. F. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00115 · Cited by 41
Optimizing Strategies for Developing Genetically Encoded Voltage Indicators
Kannan, M., Vasan, G., & Pieribone, V. A. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00053 · Cited by 43
Park, S., Na, K., Vöröslakos, M., Song, H., Slager, N., Oh, S., Seymour, J. P., Buzsáki, G., & Yoon, E. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3093542 · Cited by 40
García‐Etxarri, A., & Yuste, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01270-9 · Cited by 40
Bridging neuronal correlations and dimensionality reduction
Umakantha, A., Morina, R., Cowley, B. R., Snyder, A. C., Smith, M. A., & Yu, B. M. (2021) · Neuron · doi:10.1016/j.neuron.2021.06.028 · Cited by 40
Deep brain stimulation for Parkinson’s Disease: A Review and Future Outlook
Malvea, A., Babaei, F., Boulay, C., Sachs, A. J., & Park, J. (2022) · Biomedical Engineering Letters · doi:10.1007/s13534-022-00226-y · Cited by 39
Rimehaug, A. E., Stasik, A. J., Hagen, E., Billeh, Y. N., Siegle, J. H., Dai, K., Olsen, S. R., Koch, C., Einevoll, G. T., & Arkhipov, A. S. (2023) · eLife · doi:10.7554/elife.87169 · Cited by 41
Self-citationEngineering brain-on-a-chip platforms
Servais, B., Mahmoudi, N., Gautam, V., Tong, W., Ibbotson, M. R., Nisbet, D. R., & Collins, D. J. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00184-3 · Cited by 44
Top-down modulation of the retinal code via histaminergic neurons of the hypothalamus
Warwick, R. A., Riccitelli, S., Heukamp, A. S., Yaakov, H., Swain, B. P., Ankri, L., Mayzel, J., Gilead, N., Parness-Yossifon, R., Marco, S. D., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk4062 · Cited by 42
Multiplexed subspaces route neural activity across brain-wide networks
MacDowell, C. J., Libby, A., Jahn, C. I., Tafazoli, S., Ardalan, A., & Buschman, T. J. (2025) · Nature Communications · doi:10.1038/s41467-025-58698-2 · Cited by 49
Harris, S., Ellingford, R. A., Hartmann, J., Dasgupta, D., Kehring, M., Rajani, R. M., Graykowski, D. R., Quittot, N., Sivasankaran, D., Commins, C., et al. (2025) · Cell · doi:10.1016/j.cell.2025.04.006 · Cited by 43
Multiplexed neural sensor array of graphene solution-gated field-effect transistors
Schaefer, N., Garcia‐Cortadella, R., Martínez-Aguilar, J., Schwesig, G., Illa, X., Moya, A., Santiago, S., Hébert, C., Guirado, G., Villa, R., et al. (2020) · 2D Materials · doi:10.1088/2053-1583/ab7976 · Cited by 42
Bidirectional Bioelectronic Interfaces: System Design and Circuit Implications
Liu, Y., Urso, A., Ponte, R. M. D., Costa, T. L., Valente, V., Giagka, V., Serdijn, W. A., Constandinou, T. G., & Denison, T. (2020) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2020.2987506 · Cited by 40
Computational challenges and opportunities for a bi-directional artificial retina
Shah, N. P., & Chichilnisky, E. J. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/aba8b1 · Cited by 39
Leung, A., Cohen, D., van Swinderen, B., & Tsuchiya, N. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008722 · Cited by 42
The diversity and specificity of functional connectivity across spatial and temporal scales
Engel, T. A., Schölvinck, M. L., & Lewis, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118692 · Cited by 42
Marques–Smith, A., Neto, J. P., Lopes, G., Nogueira, J., Calcaterra, L., Frazão, J., Kim, D., Phillips, M. G., Dimitriadis, G., & Kampff, A. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/370080 · Cited by 39
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
Silicon microfabrication technologies for biology integrated advance devices and interfaces
Juska, V. B., Maxwell, G. D., Estrela, P., Pemble, M. E., & O'Riordan, A. J. (2023) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2023.115503 · Cited by 38
Neural landscape diffusion resolves conflicts between needs across time
Richman, E. B., Ticea, N., Allen, W. E., Deisseroth, K. A., & Luo, L. (2023) · Nature · doi:10.1038/s41586-023-06715-z · Cited by 41
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Xia, F., Fascianelli, V., Vishwakarma, N., Ghinger, F. G., Kwon, A., Gergues, M. M., Lalani, L. K., Fusi, S., & Kheirbek, M. A. (2024) · Nature · doi:10.1038/s41586-024-08241-y · Cited by 38
Gao, W., Yan, Z., Zhou, H., Xie, Y., Wang, H., Yang, J., Yu, J., Ni, C., Liu, P., Xie, M., et al. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03573-x · Cited by 38
Thompson, C. H., Riggins, T. E., Patel, P. R., Chestek, C. A., Li, W., & Purcell, E. K. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7030 · Cited by 38
Mapping Functional Connectivity from the Dorsal Cortex to the Thalamus
Huo, Y., Chen, H., & Guo, Z. V. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.038 · Cited by 38
Mesoscale microscopy and image analysis tools for understanding the brain
Tyson, A. L., & Margrie, T. W. (2021) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2021.06.013 · Cited by 39
Lim, J., Lee, J., Moon, E., Barrow, M., Atzeni, G., Letner, J. G., Costello, J. T., Nason, S. R., Patel, P. R., Sun, Y., et al. (2022) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2022.3141688 · Cited by 38
Distributed implantation of a flexible microelectrode array for neural recording
Wei, C., Wang, Y., Pei, W., Han, X., Lin, L., Liu, Z., Ming, G., Chen, R., Wu, P., Yang, X., et al. (2022) · Microsystems & Nanoengineering · doi:10.1038/s41378-022-00366-2 · Cited by 38
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Yang, X., Ballini, M., Sawigun, C., Hsu, W.-Y., Weijers, J.-W., Putzeys, J., & López, C. M. (2023) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2023.3234612 · Cited by 38
Self-citationBonizzato, M., Hottin, R. G., Côté, S. L., Massai, E., Choinière, L., Macar, U., Laferrière, S., Sirpal, P., Quessy, S., Lajoie, G., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101008 · Cited by 41
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Zhou, Y., Yang, H., Wang, X., Yang, H., Sun, K., Zhou, Z., Sun, L., Zhao, J., Tao, T. H., & Wei, X. (2023) · Microsystems & Nanoengineering · doi:10.1038/s41378-023-00565-5 · Cited by 37
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Li, X., Zhu, H. J., Gu, B., Yao, C., Gu, Y., Xu, W., Zhang, J., He, J., Liu, X., & Li, D. (2023) · Advanced Materials · doi:10.1002/adma.202305268 · Cited by 40
Kwon, Y. W., Ahn, D. B., Park, Y., Kim, E., Lee, D. H., Kim, S., Lee, K., Kim, W., Hong, Y., Koh, C. S., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn3784 · Cited by 37
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Jung, T., Zeng, N., Fabbri, J. D., Eichler, G., Li, Z., Zabeh, E., Das, A., Willeke, K. F., Wingel, K., Dubey, A., et al. (2025) · Nature Electronics · doi:10.1038/s41928-025-01509-9 · Cited by 40
Perceptual Decision-Making: A Field in the Midst of a Transformation
Najafi, F., & Churchland, A. K. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.017 · Cited by 41
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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Burns, T. C., & Quiñones‐Hinojosa, A. (2021) · BMJ · doi:10.1136/bmj.n955 · Cited by 36
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Wu, X., Yang, X., Song, L., Wang, Y., Li, Y., Liu, Y., Yang, X., Wang, Y., Pei, W., & Li, W. (2021) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2021.682019 · Cited by 37
Currie, S. P., Ammer, J. J., Premchand, B., Dacre, J., Wu, Y., Eleftheriou, C., Colligan, M., Clarke, T. R., Mitchell, L., Faisal, A. A., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110801 · Cited by 37
Semi-Implantable Bioelectronics
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
A scale-dependent measure of system dimensionality
Recanatesi, S., Bradde, S., Balasubramanian, V., Steinmetz, N. A., & Shea‐Brown, E. (2022) · Patterns · doi:10.1016/j.patter.2022.100555 · Cited by 37
Self-citationA stable, distributed code for cue value in mouse cortex during reward learning
Ottenheimer, D. J., Hjort, M. M., Bowen, A. J., Steinmetz, N. A., & Stuber, G. D. (2023) · eLife · doi:10.7554/elife.84604 · Cited by 41
Self-citationKetamine evoked disruption of entorhinal and hippocampal spatial maps
Masuda, F. K., Jones, E. A. A., Sun, Y., & Giocomo, L. M. (2023) · Nature Communications · doi:10.1038/s41467-023-41750-4 · Cited by 37
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Badhiwala, K. N., Gonzales, D. L., Vercosa, D. G., Avants, B. W., & Robinson, J. T. (2018) · Lab on a Chip · doi:10.1039/c8lc00475g · Cited by 36
Metadata
- Authors
- Jun, J. J., Steinmetz, N. A., Siegle, J. H., Denman, D. J., Bauza, M., Barbarits, B., Lee, A. K., Anastassiou, C. A., Andrei, A., Aydın, Ç., Barbic, M., Blanche, T. J., Bonin, V., Couto, J., Dutta, B., Gratiy, S. L., Gutnisky, D. A., Häusser, M., Karsh, B., Ledochowitsch, P., Lopez, C. M., Mitelut, C., Musa, S., Okun, M., Pachitariu, M., Putzeys, J., Rich, P. D., Rossant, C., Sun, W.-L., Svoboda, K., Carandini, M., Harris, K. D., Koch, C., O’Keefe, J., & Harris, T. D.
- Affiliations
- Howard Hughes Medical Institute (US)
- UCL Queen Square Institute of Neurology (GB)
- Allen Institute for Brain Science (US)
- IMEC (BE)
- Neuroelectronics Research Flanders (BE)
- University College London (GB)
- University of Leicester (GB)
- Corresponding author
- Harris, Timothy D.
- ORCID
- Steinmetz, Nicholas A.
- Siegle, Joshua H.
- Denman, Daniel J.
- Lee, Albert K.
- Anastassiou, Costas A.
- Andrei, Alexandru
- Aydın, Çağatay
- Barbic, Mladen
- Bonin, Vincent
- Couto, João
- Gratiy, Sergey L.
- Gutnisky, Diego A.
- Häusser, Michael
- Karsh, Bill
- Ledochowitsch, Peter
- Mitelut, Catalin
- Okun, Michael
- Pachitariu, Marius
- Putzeys, Jan
- Rossant, Cyrille
- Svoboda, Karel
- Carandini, Matteo
- Harris, Timothy D.
- Venue
- Nature
- Published
- 2017-11-09
- Volume
- 551
- Issue
- 7679
- Pages
- 232-236
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5955206
- OpenAlex
- W2767493192
- Cited by
- 2,190
- References
- 33
- Citations by year
- 2026: 229 · 2025: 334 · 2024: 269 · 2023: 297 · 2022: 232 · 2021: 289 · 2020: 221 · 2019: 199 · 2018: 116 · 2017: 2
- FWCI
- 71.96
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion, other-oa)
- Funders
- Howard Hughes Medical Institute
- Wellcome Trust (203020/Z/16/Z, 095668, 095669, 100154)
- Academy of Medical Sciences (SBF002\1045)
- Biotechnology and Biological Sciences Research Council (BB/P020607/1)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- Neuropixels, silicon probes, high-density neural recording, extracellular recording, single-unit recording, freely moving animals
- MeSH terms
- Animals, Electrodes, Female, Male, Movement, Neurons, Semiconductors, Silicon, Wakefulness, Prefrontal Cortex, Entorhinal Cortex, Mice, Rats
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