The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
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.
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?
In the citing paper
“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
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
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
Flagged first, then by citations
1 of the 33 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Musa, S. Titanium nitride electrode. US patent 9,384,990 (2016)
Cited by
Sorted by citations
Items 801–900 of 1,004
Zhou, Y., Ji, B., Wang, M., Zhang, K., Huangfu, S., Feng, H., Chang, H., & Yuan, X. (2021) · Coatings · doi:10.3390/coatings11020204 · Cited by 19
Scaling the Poisson GLM to massive neural datasets through polynomial approximations.
Zoltowski, D. M., & Pillow, J. W. (2018) · PubMed · Cited by 22
MEMS-Actuated Carbon Fiber Microelectrode for Neural Recording
Zoll, R. S., Schindler, C. B., Massey, T. L., Drew, D., Maharbiz, M. M., & Pister, K. S. J. (2019) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2019.2905505 · Cited by 18
Recent trends in morbidity and mortality rates for abdominal aortic aneurysms.
Semenciw, R. M., MORRISON, H. I., Wigle, D., Cole, W., & Hill, G. (1992) · PubMed · Cited by 18
Montijn, J. S., & Heimel, J. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.20.496782 · Cited by 18
Reproducibility of in vivo electrophysiological measurements in micePreprint
Banga, K., Benson, J., Bhagat, J., Biderman, D., Birman, D., Bonacchi, N., Bruijns, S. A., Buchanan, K. L., Campbell, R. A. A., Carandini, M., et al. (2024) · eLife · doi:10.7554/elife.100840 · Cited by 21
Self-citationCompression strategies for large-scale electrophysiology data
Buccino, A. P., Winter, O., Bryant, D., Feng, D. H., Svoboda, K., & Siegle, J. H. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acf5a4 · Cited by 17
Self-citationBonding methods for chip integration with Parylene devices
Yoo, J. J., & Meng, E. F. (2021) · Journal of Micromechanics and Microengineering · doi:10.1088/1361-6439/abe246 · Cited by 17
Tian, J., Lin, Z., Chen, Z., Obaid, S. N., Efimov, I. R., & Lu, L. (2021) · Photonics · doi:10.3390/photonics8060220 · Cited by 17
Feature-Selection-Based Transfer Learning for Intracortical Brain–Machine Interface Decoding
Zhang, P., Li, W., Ma, X., He, J., Huang, J., & Li, Q. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3034234 · Cited by 17
Graph-Based Change-Point Analysis
Chen, H., & Chu, L. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-122121-033817 · Cited by 22
Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex
Wei, Y., Nandi, A., Jia, X., Siegle, J. H., Denman, D. J., Lee, S. Y., Buchin, A., Geit, W. A. H. V., Mosher, C. P., Olsen, S. R., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37844-8 · Cited by 18
Self-citationRecent developments in implantable neural probe technologies
Chen, H., & Fang, Y. Z. (2023) · MRS Bulletin · doi:10.1557/s43577-023-00535-2 · Cited by 18
Advanced materials for implantable neuroelectronics
Qi, Y., Kang, S., Fang, H., & Editors, G. (2023) · MRS Bulletin · doi:10.1557/s43577-023-00540-5 · Cited by 17
Electrode Development for Epilepsy Diagnosis and Treatment
Paulk, A. C., Salami, P., Zelmann, R., & Cash, S. S. (2023) · Neurosurgery Clinics of North America · doi:10.1016/j.nec.2023.09.003 · Cited by 17
Full field-of-view virtual reality goggles for mice
Pinke, D., Issa, J. B., Dara, G., Dobos, G., & Dombeck, D. A. (2023) · Neuron · doi:10.1016/j.neuron.2023.11.019 · Cited by 21
Londei, F., Arena, G., Ferrucci, L., Russo, E., Ceccarelli, F., & Genovesio, A. (2023) · iScience · doi:10.1016/j.isci.2023.108761 · Cited by 17
Chronic, Reusable, Multiday Neuropixels Recordings during Free-Moving Operant Behavior
Song, Z., Alpers, A., Warner, K., Iacobucci, F. A., Hoskins, E., Disterhoft, J. F., Voss, J. L., & Widge, A. S. (2024) · eNeuro · doi:10.1523/eneuro.0245-23.2023 · Cited by 17
Structure of activity in multiregion recurrent neural networks
Clark, D. G., & Beirán, M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404039122 · Cited by 18
Xu, Q., Xi, Y., Wang, L., Du, Z., Xu, M., Ruan, T., Cao, J., Zheng, K., Wang, X., Yang, B. T., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c07379 · Cited by 19
Hope, J., Beckerle, T., Cheng, P.-H., Viavattine, Z., Feldkamp, M., Fausner, S. M. L., Saxena, K., Ko, E., Hryb, I., Carter, R. E., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02434-z · Cited by 18
Bukwich, M., Campbell, M. G., Zoltowski, D. M., Kingsbury, L., Tomov, M. S., Stern, J., Kim, H. R., Drugowitsch, J., Linderman, S. W., & Uchida, N. (2025) · Neuron · doi:10.1016/j.neuron.2025.07.008 · Cited by 20
Real-time Closed Loop Neural Decoding on a Neuromorphic chip
Shaikh, S., So, R. Q., Sibindi, T. M., Libedinsky, C., & Basu, A. (2019) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2019.8717122 · Cited by 17
Brain-wide, scale-wide physiology underlying behavioral flexibility in zebrafish
Mu, Y., Narayan, S., Mensh, B. D., & Ahrens, M. B. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.08.013 · Cited by 19
Maes, A., Barahona, M., & Clopath, C. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008866 · Cited by 18
Novais, A., Calaza, C., Fernandes, J., Fonseca, H., Monteiro, P., Gaspar, J., & Jacinto, L. (2021) · Sensors · doi:10.3390/s21082605 · Cited by 17
Cecchetto, C., Vassanelli, S., & Kühn, B. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.741279 · Cited by 17
Galvanic-Coupled Trans-Dural Data Transfer for High-Bandwidth Intracortical Neural Sensing
Shi, C., Song, M., Gao, Z., Bevilacqua, A., Dolmans, G., & Liu, Y. (2022) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2022.3198100 · Cited by 17
MEDiCINe: Motion Correction for Neural Electrophysiology Recordings
Watters, N., Buccino, A. P., & Jazayeri, M. (2025) · eNeuro · doi:10.1523/eneuro.0529-24.2025 · Cited by 16
Low-cost and versatile electrodes for extracellular chronic recordings in rodents
França, A. S. C., van Hulten, J. A., & Cohen, M. X. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04867 · Cited by 16
Sibille, J., Gehr, C., Teh, K. L., & Kremkow, J. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109622 · Cited by 16
Real‐time fiber‐optic recording of acute‐ischemic‐stroke signatures
Pochechuev, M. S., Bilan, D. S., Fedotov, I. V., Kelmanson, I. V., Solotenkov, M. A., Stepanov, E. A., Kotova, D. A., Ivanova, A. D., Kostyuk, A. I., Raevskii, R. I., et al. (2022) · Journal of Biophotonics · doi:10.1002/jbio.202200050 · Cited by 16
Nakuci, J., Covey, T. J., Shucard, J. L., Shucard, D. W., & Muldoon, S. F. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119895 · Cited by 16
Huerta, T. S., Haider, B., Adamovich‐Zeitlin, R., Chen, A. C., Chaudhry, S., Zanos, T. P., Chavan, S. S., Tracey, K. J., & Chang, E. H. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acbe1e · Cited by 16
Gehr, C., Sibille, J., & Kremkow, J. (2023) · eLife · doi:10.7554/elife.88289 · Cited by 20
Awake responses suggest inefficient dense coding in the mouse retina
Boissonnet, T., Tripodi, M., & Asari, H. (2023) · eLife · doi:10.7554/elife.78005 · Cited by 16
Multi-day neuron tracking in high-density electrophysiology recordings using earth mover’s distance
Yuan, A., Colonell, J. I., Lebedeva, A., Okun, M., Charles, A. S., & Harris, T. D. (2023) · eLife · doi:10.7554/elife.92495 · Cited by 17
Self-citationFlexible and Stretchable Electrodes for In Vivo Electrophysiological and Electrochemical Monitoring†
Sheng, K., Lu, Y., Fan, W., & Liu, Y. (2024) · Chinese Journal of Chemistry · doi:10.1002/cjoc.202300652 · Cited by 16
Ishizu, K., Nishimoto, S., Ueoka, Y., & Funamizu, A. (2024) · Nature Communications · doi:10.1038/s41467-024-48338-6 · Cited by 19
Starting a synthetic biological intelligence lab from scratch
Tanveer, M. S., Patel, D., Schweiger, H. E., Abu-Bonsrah, K. D., Watmuff, B., Azadi, A., Pryshchep, S., Narayanan, K., Puleo, C., Natarajan, K., et al. (2025) · Patterns · doi:10.1016/j.patter.2025.101232 · Cited by 17
Yin, C., Melin, M. D., Rojas-Bowe, G., Sun, X. R., Couto, J., Gluf, S., Kostiuk, A., Musall, S., & Churchland, A. K. (2025) · Neuron · doi:10.1016/j.neuron.2025.06.001 · Cited by 23
Self-citationReplay and representation dynamics in the hippocampus of freely flying bats
Forli, A., Fan, W., Qi, K. K., & Yartsev, M. M. (2025) · Nature · doi:10.1038/s41586-025-09341-z · Cited by 16
Recurrent pattern completion drives the neocortical representation of sensory inference
Shin, H., Ogando, M. B., Abdeladim, L., Jagadisan, U. K., Durand, S., Hardcastle, B., Belski, H., Cabasco, H., Loeffler, H., Bawany, A., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02055-5 · Cited by 19
Charles, A. S., Falk, B., Turner, N. L., Pereira, T., Tward, D. J., Pedigo, B. D., Chung, J., Burns, R. C., Ghosh, S., Kebschull, J. M., et al. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100119-110036 · Cited by 16
Decentralized Motion Inference and Registration of Neuropixel Data
Varol, E., Boussard, J., Dethe, N., Winter, O., Urai, A. E., Laboratory, I. B., Churchland, A. K., Steinmetz, N. A., & Paninski, L. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414145 · Cited by 16
Self-citationFrancoeur, M. J., Tang, T., Fakhraei, L., Wu, X., Hulyalkar, S., Cramer, J., Buscher, N., & Ramanathan, D. R. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.678103 · Cited by 16
Boschi, A., Brofiga, M., & Massobrio, P. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.705103 · Cited by 18
Chou, N., Shin, H., Kim, K., Chae, U., Jang, M., Jeong, U., Hwang, K. S., Yi, B., Lee, S. E., Woo, J., et al. (2021) · Advanced Science · doi:10.1002/advs.202103564 · Cited by 16
Deconvolution improves the detection and quantification of spike transmission gain from spike trains
Spivak, L., Levi, A., Sloin, H. E., Someck, S., & Stark, E. (2022) · Communications Biology · doi:10.1038/s42003-022-03450-5 · Cited by 16
Letner, J. G., Patel, P. R., Hsieh, J., Flores, I. M. S., della Valle, E., Walker, L. A., Weiland, J. D., Chestek, C. A., & Cai, D. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acbf78 · Cited by 16
Cellular-scale silicon probes for high-density, precisely localized neurophysiology
Egert, D., Pettibone, J. R., Lemke, S. M., Patel, P. R., Caldwell, C. M., Cai, D., Ganguly, K., Chestek, C. A., & Berke, J. D. (2020) · Journal of Neurophysiology · doi:10.1152/jn.00352.2020 · Cited by 16
Boi, F., Perentos, N., Lecomte, A., Schwesig, G., Zordan, S., Sirota, A., Berdondini, L., & Angotzi, G. N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/749911 · Cited by 16
Running modulates primate and rodent visual cortex differentlyPreprint
Liska, J. P., Rowley, D. P., Nguyen, T. T. K., Muthmann, J.-O., Butts, D. A., Yates, J. L., & Huk, A. C. (2023) · eLife · doi:10.7554/elife.87736 · Cited by 15
Electrode Arrays for Detecting and Modulating Deep Brain Neural Information in Primates: A Review
Zhang, S., Song, Y., Lv, S., Jing, L., Wang, M., Liu, Y., Xu, W., Jiao, P., Zhang, S., Wang, M., et al. (2025) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0249 · Cited by 16
Yan, P., Akhoundi, A., Shah, N. P., Tandon, P., Muratore, D. G., Chichilnisky, E. J., & Murmann, B. (2023) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2023.3292058 · Cited by 15
Convergence of Implantable Bioelectronics and Brain–Computer Interfaces
Huynh, M. A., Nguyen, T.-H., Nguyen, T.-K., & Nguyen, N.-T. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c00879 · Cited by 15
Gao, L., Lv, S., Shang, Y., Guan, S., Tian, H., Fang, Y. Z., Wang, J., & Li, H. (2023) · Nano Letters · doi:10.1021/acs.nanolett.3c03421 · Cited by 15
Representational maps in the brain: concepts, approaches, and applications
Noda, T., Aschauer, D. F., Chambers, A. R., Seiler, J. P.-H., & Rumpel, S. (2024) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2024.1366200 · Cited by 16
Papadopoulos, L., Jo, S., Zumwalt, K., Wehr, M., Jaramillo, S., McCormick, D. A., & Mazzucato, L. (2025) · Neuron · doi:10.1016/j.neuron.2025.11.011 · Cited by 16
López‐Jury, L., Meza, R. C., Brown, M. T. C., Henny, P., & Canavier, C. C. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0651-18.2018 · Cited by 15
Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals
Mahmud, M., & Vassanelli, S. (2019) · Advances in neurobiology · doi:10.1007/978-3-030-11135-9_10 · Cited by 15
Vandekerckhove, B., Missinne, J., Vonck, K. E. J., Bauwens, P., Verplancke, R., Boon, P., Raedt, R., & Vanfleteren, J. (2020) · Micromachines · doi:10.3390/mi12010038 · Cited by 17
Casile, A., Faghih, R. T., & Brown, E. N. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007675 · Cited by 15
Cao, L., Varga, V., & Chen, Z. S. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100101 · Cited by 16
Projections of the Mouse Primary Visual Cortex
Resulaj, A. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.751331 · Cited by 19
Neurophysiology of the Developing Cerebral Cortex: What We Have Learned and What We Need to Know
Luhmann, H. J. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.814012 · Cited by 16
Lin, Y.-J., Song, H., Oh, S., Vöröslakos, M., Kim, K., Chen, X., Wentzloff, D. D., Buzsáki, G., Park, S., & Yoon, E. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2021.3139891 · Cited by 15
Ren, W., Ji, B., Guan, Y., Cao, L., & Ni, R. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.771982 · Cited by 16
Xu, X., Xie, S., Zhang, Y., & Peng, H. (2019) · Angewandte Chemie · doi:10.1002/ange.201902425 · Cited by 15
Scalable Three-Dimensional Recording Electrodes for Probing Biological Tissues
Lee, J. M., Lin, D., Hong, G., Kim, K., Park, H., & Lieber, C. M. (2022) · Nano Letters · doi:10.1021/acs.nanolett.2c01444 · Cited by 15
In Vivo Neural Interfaces—From Small- to Large-Scale Recording
Zhang, B., Deng, C., Cai, C., & Li, X. (2022) · Frontiers in Nanotechnology · doi:10.3389/fnano.2022.885411 · Cited by 15
Stitching Flexible Electronics into the Brain
Lee, J. M., Lin, D., Pyo, Y., Kim, H., Park, H., & Lieber, C. M. (2023) · Advanced Science · doi:10.1002/advs.202300220 · Cited by 15
POEMS (Polymeric Opto-Electro-Mechanical Systems) for advanced neural interfaces
Kampasi, K., Ladner, I., Zhou, J. J., Calonico-Soto, A., Hernandez, J. E., Patra, S. K., & Haque, R. (2020) · Materials Letters · doi:10.1016/j.matlet.2020.129015 · Cited by 15
Neural System Identification With Spike-Triggered Non-Negative Matrix Factorization
Jia, S., Yu, Z., Onken, A., Tian, Y., Huang, T., & Liu, J. K. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3042513 · Cited by 15
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational InferencePreprint
Hurwitz, C., Xu, K., Srivastava, A., Buccino, A. P., & Hennig, M. H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/656389 · Cited by 15
DataJoint Elements: Data Workflows for NeurophysiologyPreprint
Yatsenko, D., Nguyen, T. P., Shen, S., Gunalan, K., Turner, C. A., Guzman, R., Sasaki, M., Sitonic, D., Reimer, J., Walker, E. Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.30.437358 · Cited by 15
High-Density, Actively Multiplexed µECoG Array on Reinforced Silicone Substrate
Rachinskiy, I., Wong, L., Chiang, C.-H., Wang, C., Trumpis, M., Ogren, J. I., Hu, Z., McLaughlin, B., & Viventi, J. (2022) · Frontiers in Nanotechnology · doi:10.3389/fnano.2022.837328 · Cited by 14
A circuit that integrates drive state and social contact to gate mating
Salay, L. D., Tsao, D. Y., & Anderson, D. J. (2025) · Nature · doi:10.1038/s41586-025-09327-x · Cited by 14
Inserting a Neuropixels probe into awake monkey cortex: two probes, two methods
Namima, T., Kempkes, E., Smith, B., Smith, L., Orsborn, A. L., & Pasupathy, A. (2023) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2023.110016 · Cited by 14
Optogenetic Brain–Computer Interfaces
Tang, F., Yan, F., Zhong, Y., Li, J., Gong, H., & Li, X. (2024) · Bioengineering · doi:10.3390/bioengineering11080821 · Cited by 14
Female calls promote song learning in male juvenile zebra finches
Bistere, L., Gomez-Guzman, C. M., Xiong, Y., & Vallentin, D. (2024) · Nature Communications · doi:10.1038/s41467-024-53251-z · Cited by 15
Zou, L., Chen, Z., Fang, R., Liu, X., Xu, K., Ding, J., Wang, J., Zhang, F., Fang, Y. Z., & Tian, H. (2025) · ACS Nano · doi:10.1021/acsnano.4c16490 · Cited by 14
A common computational and neural anomaly across mouse models of autism
Noel, J., Balzani, E., Acerbi, L., Benson, J., Angelaki, D. E., Birman, D., Bonacchi, N., Carandini, M., Catarino, J. A., Chapuis, G. A., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01965-8 · Cited by 18
Self-citationChronically-implanted Neuropixels probes enable high yield recordings in freely moving mice
Juavinett, A., Bekheet, G., & Churchland, A. K. (2019) · Protocol Exchange · doi:10.1038/protex.2018.100 · Cited by 14
Understanding Sensory Information Processing Through Simultaneous Multi-area Population Recordings
Zavitz, E., & Price, N. S. C. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00115 · Cited by 17
Quantity versus quality: Convergent findings in effort-based choice tasks
Hart, E. E., & Izquierdo, A. (2019) · Behavioural Processes · doi:10.1016/j.beproc.2019.05.009 · Cited by 14
Monosynaptic inference via finely-timed spikes
Platkiewicz, J., Saccomano, Z., McKenzie, S., English, D. F., & Amarasingham, A. (2021) · Journal of Computational Neuroscience · doi:10.1007/s10827-020-00770-5 · Cited by 14
Decoding Neural Responses in Mouse Visual Cortex through a Deep Neural Network
Iqbal, A., Dong, P., Kim, C. M., & Jang, H. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852121 · Cited by 15
Cui, M.-R., Zhao, W., Li, X. L., Xu, C. H., Xu, J. J., & Chen, H. (2020) · Analytica Chimica Acta · doi:10.1016/j.aca.2019.11.074 · Cited by 14
Zhang, X., Schlögl, A., Vandael, D. H. F., & Jónás, P. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109125 · Cited by 14
Three-micrometer-diameter needle electrode with an amplifier for extracellular in vivo recordings
Kita, Y., Tsuruhara, S., Kubo, H., Yamashita, K., Seikoba, Y., Idogawa, S., Sawahata, H., Yamagiwa, S., Leong, X. L. A., Numano, R., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2008233118 · Cited by 14
A data-driven spike sorting feature map for resolving spike overlap in the feature space
Wouters, J., Kloosterman, F., & Bertrand, A. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac0f4a · Cited by 14
Nakazawa, S., & Iwasato, T. (2021) · Development Growth & Differentiation · doi:10.1111/dgd.12739 · Cited by 14
Wireless Addressable Cortical Microstimulators Powered by Near-Infrared Harvesting
Lee, A., Lee, J.-H., Jang, J., Nurmikko, A. V., & Song, Y. (2021) · ACS Sensors · doi:10.1021/acssensors.1c00813 · Cited by 14
Self-citationNanoenabled Bioelectrical Modulation
Promiński, A., Li, P., Miao, B. A., & Tian, B. (2021) · Accounts of Materials Research · doi:10.1021/accountsmr.1c00132 · Cited by 14
Kim, J. H., Ryu, J. R., Lee, B., Chae, U., Son, J. W., Park, B. H., Cho, I., & Sun, W. (2021) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2021.746057 · Cited by 14
Self-citationLearning the Architectural Features That Predict Functional Similarity of Neural Networks
Haber, A., & Schneidman, E. (2022) · Physical Review X · doi:10.1103/physrevx.12.021051 · Cited by 14
Fear Learning: An Evolving Picture for Plasticity at Synaptic Afferents to the Amygdala
Palchaudhuri, S., Osypenko, D., & Schneggenburger, R. (2022) · The Neuroscientist · doi:10.1177/10738584221108083 · Cited by 15
How to incorporate biological insights into network models and why it matters
Timón, L. B., Ekelmans, P., Kraynyukova, N., Rose, T., Busse, L., & Tchumatchenko, T. (2022) · The Journal of Physiology · doi:10.1113/jp282755 · Cited by 17
Brainstem networks construct threat probability and prediction error from neuronal building blocks
Strickland, J. A., & McDannald, M. A. (2022) · Nature Communications · doi:10.1038/s41467-022-34021-1 · Cited by 14
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
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