The DOI matches its Crossref record; 158 of the 158 references it cites were found in a registry or an index.
Novel electrode technologies for neural recordings
Open accessAbstract
Neural recording electrode technologies have contributed considerably to neuroscience by enabling the extracellular detection of low-frequency local field potential oscillations and high-frequency action potentials of single units. Nevertheless, several long-standing limitations exist, including low multiplexity, deleterious chronic immune responses and long-term recording instability. Driven by initiatives encouraging the generation of novel neurotechnologies and the maturation of technologies to fabricate high-density electronics, novel electrode technologies are emerging. Here, we provide an overview of recently developed neural recording electrode technologies with high spatial integration, long-term stability and multiple functionalities. We describe how these emergent neurotechnologies can approach the ultimate goal of illuminating chronic brain activity with minimal disruption of the neural environment, thereby providing unprecedented opportunities for neuroscience research in the future.
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 cited source says what the claim attributes to it.
In the citing paper
“Similarly, both in academic and industry settings, approaches are being developed to improve throughput, sampling, biocompatibility and stability of MEAs183–186.”
From The speech neuroprosthesis (2024) · cites this paper as [185]
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
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat
O'Keefe, J., & Dostrovsky, J. (1971) · Brain Research · doi:10.1016/0006-8993(71)90358-1 · Cited by 6,151
Nanowire Nanosensors for Highly Sensitive and Selective Detection of Biological and Chemical Species
Cui, Y., Wei, Q., Park, H., & Lieber, C. M. (2001) · Science · doi:10.1126/science.1062711 · Cited by 5,857
Self-citationReceptive fields of single neurones in the cat's striate cortex
Hubel, D. H., & Wiesel, T. N. (1959) · The Journal of Physiology · doi:10.1113/jphysiol.1959.sp006308 · Cited by 4,653
The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes
Buzsáki, G., Anastassiou, C. A., & Koch, C. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3241 · Cited by 4,225
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
Theta oscillations in the hippocampus
Buzsáki, G. (2002) · Neuron · doi:10.1016/s0896-6273(02)00586-x · Cited by 3,332
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
Near-infrared fluorophores for biomedical imaging
Hong, G., Antaris, A. L., & Dai, H. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-016-0010 · Cited by 3,048
Self-citation
Research works printed but not linked: 3
- Carter, M. & Shieh, J. C. Guide to Research Techniques in Neuroscience (Academic Press, 2015).
- Kandel, E. R. Principles of Neural Science (McGraw-Hill, 2013).
- Johnson, B. C. et al. in 2018 IEEE Custom Integrated Circuits Conf. (CICC) 1–4 (IEEE, 2018).
Cited by
Sorted by citations
Items 601–694 of 694
A figure of merit for noise power and area optimization of neural amplifiersPreprint
Sharma, S., hande, V. G., & Das, D. M. (2023) · doi:10.36227/techrxiv.22219579
A figure of merit for noise power and area optimization of neural amplifiersPreprint
Sharma, S., hande, V. G., & Das, D. M. (2023) · doi:10.36227/techrxiv.22219579.v1
A figure of merit for noise power and area optimization of neural amplifiersPreprint
Sharma, S., hande, V. G., & Das, D. M. (2023) · doi:10.36227/techrxiv.22219579.v2
eLife Assessment: Neuroelectrophysiology-Compatible Electrolytic Lesioning
Behrens, T. E. J. (2023) · eLife · doi:10.7554/elife.84385.1.sa3
Author Response: Neuroelectrophysiology-Compatible Electrolytic Lesioning
Bray, I. E., Clarke, S. E., Casey, K. M., & Nuyujukian, P. (2023) · eLife · doi:10.7554/elife.84385.1.sa0
Xiao, D., Yan, Y., & Murphy, T. H. (2023) · eLife · doi:10.7554/elife.87691.1
Sheng, X., Wenxin, 赵. Z., Lizhu, 李. L., Yunxiang, 黄. H., & Ding, H. (2023) · Chinese Journal of Lasers · doi:10.3788/cjl221562
Neurobiology and the connectome
Ciarrusta, J., & Arichi, T. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00012-9
J. G. Grey Oration: Civil-Military Relations in an age of disruption
Cohen, E. A. (2019) · Australian Journal of Defence and Strategic Studies · doi:10.51174/ajdss.0101/scsp4549
3D Printable Hydrogel Bioelectronic Interfaces for Various Organs
Zhou, T. (2023) · IEEE International Flexible Electronics Technology Conference (IFETC) · doi:10.1109/ifetc57334.2023.10254791
Ling, Q. (2023) · Journal of Biomedical Nanotechnology · doi:10.1166/jbn.2023.3562
A study on the characteristics of CNT for the development of a long-term neural recording
Wardcharoen, K., Kanno, S., Shimba, K., Miyamoto, Y., & Yagi, T. (2023) · Biomedical Engineering International Conference · doi:10.1109/bmeicon60347.2023.10322019
Hydrogel‐Based Sensing for Living Biosystems
Zhao, Y., Pan, Y., & Yu, P. (2024) · Wiley eBooks · doi:10.1002/9780470027318.a9815
Fabrication of a Low-cost Multi-electrode Neural Probe for Brain Signal Recording
Irandoost, A., Mohajeri, R., Bahramani, A., Ghazizadeh, A., & Fardmanesh, M. (2023) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme61513.2023.10488585
Wardcharoen, K., Kanno, S., Shimba, K., Miyamoto, Y., & Yagi, T. (2024) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.144.467
Xiao, D., Yan, Y., & Murphy, T. H. (2023) · eLife · doi:10.7554/elife.87691.1.sa3
Editorial: Rising stars in systems neuroscience: 2022
Fallah, M., Haam, J., & Ledonne, A. (2024) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2024.1414351
Brain implantation of tissue-level-soft bioelectronics via embryonic developmentPreprint
Sheng, H., Liu, R., Li, Q., Lin, Z., He, Y., Blum, T. S., Zhao, H., Tang, X., Wang, W., Jin, L., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.29.596533
Neuroelectrophysiology-Compatible Electrolytic LesioningPreprint
Bray, I. E., Clarke, S. E., Casey, K. M., & Nuyujukian, P. (2024) · eLife · doi:10.7554/elife.84385.2 · Cited by 1
Rezayat, L., Ghajar, M. H., Naji, A., Noroozi, J., Dehaqani, M. A., & Rezayat, E. (2024) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad63ad
Fernández, J. A. C. (2022) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-413348
Ande, S., Avasarala, S., Swain, S., Karunarathne, A., Giri, L., & Jana, S. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad6cf4
All-Diamond Boron-Doped Microelectrodes for Neurochemical Sensing with Fast-Scan Cyclic VoltammetryPreprint
Gupta, B., Kepros, B., Landgraf, J. B., Becker, M., Li, W., Purcell, E. K., & Siegenthaler, J. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.07.606919
He, B., Zhang, Z., Yang, J., An, Y., Guo, Y., & Yang, G. (2024) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma61710.2024.10632919
Vadeghani, E. M., Moallemi, A., & Shahriari, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95730-4.00005-6
Advanced Fibers for Optogenetic Modulation
Du, M., & Zhou, S. (2024) · Wiley eBooks · doi:10.1002/9783527839322.ch7
Visual coding along multiple brain areas
Xavier, V. D. A., Melo, N. D. S., Ribeiro, S. T. G., & de Vasconcelos, N. A. P. (2024) · International journal of psychological research · doi:10.21500/20112084.7390
Montelisciani, L., Dijkema, E. B., & Olcese, U. (2024) · Neuromethods · doi:10.1007/978-1-0716-4208-5_1
Soft Materials for Wearable Electronics
Roy, A., & Adhikari, A. (2024) · Advanced structured materials · doi:10.1007/978-981-97-9468-3_11
Methods for In Vivo Circuit Analysis
Johnson, M., Filali, Y., Velamuri, R., Dzirasa, K., & Hultman, R. C. (2025) · doi:10.1093/med/9780197640654.003.0007
RETRACTED: Monolithic three-dimensional neural probes from deterministic rolling of soft electronicsPreprint
Qiang, Y., & Fang, H. (2025) · Research Square · doi:10.21203/rs.3.rs-6051081/v1
RetractedSelf-Assembled Ultra-Flexible Mesh Probes for Stable Neural Recordings
Tu, K., Wang, L., Yang, B. T., & Liu, J. (2025) · IEEE International Conference on Micro Electro Mechanical Systems (MEMS) · doi:10.1109/mems61431.2025.10917865
Relevance of UMAP for efficient and robust spike sortingPreprint
Suárez-Barrera, D., Bayones, L., Parra, S., Monroy, V., Pujalte, S., Andrade-Ortega, B., Díaz, H., Álvarez, M., Zainos, A., Franci, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.27.645844
Chronic recording of brain activity in awake toads
Shaykevich, D. A., Woods, G. A., O’Connell, L. A., & Hong, G. (2024) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2025.110449 · Cited by 1
Self-citation“Tone-Arm” Neuromodulation Mems Microcoil for In-Vivo ImagingPreprint
Liu, X., Chang, B., Kim, K., Anand, G. A. E., Genelioglu, S., Cai, C., & Han, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5238264
Wang, X., Liu, M., Ding, J., Gu, W., Tian, H., Fang, Y. Z., Guan, S., & Wang, J. (2025) · Nano Letters · doi:10.1021/acs.nanolett.5c01662
Liu, Z., Li, J., Fu, Y., Yu, S., Li, W., Yang, Y., Wang, J., Yu, S., Yu, P., Xu, K., et al. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202501550
Fabrication, Electrochemical Characterization, and In Vivo Validation of a Flexible Neural Probe
Freitas, J. R., Rodrigues, J. A., Machado, J. L., Aguiar, L. A. A., Rodrigues, A. J., Oliveira, J. F., Correia, J. H., & Pimenta, S. (2025) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2025.3579544
Packaging and Integration of Multifunctional Brain Computer Interface
Jia, Z., Cerpa, A. D., Kim, G. J., & Yoon, Y. (2025) · IEEE Electronic Components and Technology Conference (ECTC) · doi:10.1109/ectc51687.2025.00289
Haider, B., Middya, S., Sanchez, D. J. L.-D., Läubli, N. F., Vora, S., Träuble, J., Krajeski, R. N., Feng, Y., Duncko, D., Riley, J. S., et al. (2026) · Advanced Science · doi:10.1002/advs.202519893
Han, H., Sim, S., Kim, C., Im, M., & Kim, J. (2025) · International Conference on Solid-State Sensors, Actuators and Microsystems · doi:10.1109/transducers61432.2025.11109815
Greenhorn, S., Coizet, V., Terral, O., Dupuit, V., Fernandez, B., Bres, G., Claudel, A., GASNER, P., Warnking, J. M., Barbier, E., et al. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae0d94
Curved flexible neural interfaces for laminar electrophysiology in deep-brain structuresPreprint
Hagler, J., Pizzoccaro, L., Matos-Peralta, Y., Wang, M., Ducharme, G., Amilhon, B., & Cicoira, F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.29.679383
In-vitro real-time magnetic recording of neuronal activity on spinal cord slices
Vera, A., Calaresu, I., Martínez, I., Guerrero, R., Scaini, D., Schoebel, G. D. A., Hernández, J. J., Rodríguez, I., Miranda, R., Martín, E. D., et al. (2025) · Sensing and Bio-Sensing Research · doi:10.1016/j.sbsr.2025.100885
Sasaki, H., Shimizu, S., Nerchan, R. A., Yamashita, K., Takahashi, N., Chinone, R., Kurashige, Y., Numano, R., Koida, K., Ohsawa, M., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5605796
Jin, Y., Tsai, C.-W., Achavananthadith, S., Nguyen, D. T., Xiong, Z., Liang, Z., Herikstad, R., Chuan, Y., Kurt, S. A., Ali, H. P. A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.30.685707
Sasaki, H., Shimizu, S., Nerchan, R. A., Yamashita, K., Takahashi, N., Chinone, R., Kurashige, Y., Numano, R., Koida, K., Ohsawa, M., et al. (2025) · Sensing and Bio-Sensing Research · doi:10.1016/j.sbsr.2025.100915 · Cited by 1
Aithal, P. S., Saldanha, D., Satpathy, J., & Sandhya, S., V (2025) · Poornaprajna International Journal of Emerging Technologies (PIJET) · doi:10.64818/pijet.3107.8486.0015
Huang, J., Li, X., Jiang, J., Wang, J., Zhou, S., Liang, Y., Liang, Y., Chen, X., Chen, H., Wang, H., et al. (2025) · Nanoscale · doi:10.1039/d5nr02957k
Optimized Single‐Shank Silicon Neural Probe for Neural Recording in Non‐Human Primates
Cao, J., Wang, L., Chen, H., Wang, R., Xu, Q., Xu, M., Yin, J., Wang, X., Yang, B., & Liu, J. (2025) · Advanced Materials Technologies · doi:10.1002/admt.202501356
Miniaturized Foldable Electrodes with Direct CMOS Integration for Implantable Applications
Qian, J., Coronese, M., Pafundo, A., Meimandi, A., Barbruni, G. L., Carminati, M., & Carrara, S. (2025) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas67066.2025.00084
Habit learning is associated with efficiently controlled network dynamics in naive macaque monkeys
Brynildsen, J. K., Fotiadis, P., Szymula, K. P., Kim, J. Z., Pasqualetti, F., Graybiel, A. M., Desrochers, T. M., & Bassett, D. S. (2026) · npj Complexity · doi:10.1038/s44260-025-00066-8 · Cited by 1
Sunwoo, W., & Kim, Y. (2026) · Clinical and Experimental Otorhinolaryngology · doi:10.21053/ceo.2025-00328
Cholinergic–dopaminergic synergy and antagonism shape prediction-error broadcastingPreprint
Király, B., Pillár, V., Szabó, Í., Schlingloff, D., Hegedüs, P., Szigeti, K., Li, Y., & Hangya, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.19.706866
Rolling 2D sheets into monolithic 3D neural interfaces
Min, I. S., Cho, D. W., & Yu, K. J. (2026) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ae4c2f
Plasmon-Exciton Coupled Photoluminescent Microhole Array for All-Optical Neural Recording
Gao, X., Tang, Z., Xu, J., & Zhang, M. (2025) · IEEE International Conference on Nano/Molecular Medicine and Engineering (NANOMED) · doi:10.1109/nanomed68094.2025.11431387
Physiopathologie des comas isoélectriques : de l’EEG au neurone
Carton‐Leclercq, A. (2023) · theses.fr (ABES) · doi:10.70675/7e9150a6z8959z43baza4b4zcaf0222fc163
Robotic chip-scale nanofabrication for superior consistency
Mayor, F. M., Guan, W., Szakiel, E., Safavi‐Naeini, A. H., & Gyger, S. (2026) · Applied Physics Letters · doi:10.1063/5.0313577
Yi, D., Kwankam, N., & Chen, L. (2026) · Medical Engineering & Physics · doi:10.1088/1873-4030/ae691d
Maughan, J., Woods, I., O’Connor, C., Quintana-Sarti, P., Caffrey, E., Munuera, J., Carey, T., Dervan, A. G., Valdés, A. L., Mamad, O., et al. (2026) · Advanced Healthcare Materials · doi:10.1002/adhm.202505076 · Cited by 2
Li, W., Li, X., Yang, H., Tang, C., Wang, Z., Wang, Q., Nie, Y., Liu, Z., Yang, Y., Wang, H., et al. (2026) · National Science Review · doi:10.1093/nsr/nwag325
Fluorescence-lifetime optical electrophysiology in contracting cardiomyocytes
Millar, E., Huethorst, E., Žičkus, V., Astrauskaite, G., Zhao, J., Taylor, G. G., Bruschini, C. E., Edoardo Charbon, A., Smith, G. L., Müllenbroich, C., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2533881123
Gu, W., Ju, J., & Wang, L. (2026) · Small · doi:10.1002/smll.74206
Jhang, G.-J., Tso, K.-C., Chen, K.-Y., Li, ○.-X., Liu, P.-Y., Chen, H.-Y., Sunaga, Y., Ohta, J., Sasagawa, K., Tsai, M.-F., et al. (2026) · Surface and Coatings Technology · doi:10.1016/j.surfcoat.2026.133713
Sharma, S., Shivdeep, S., Hande, V. G., & Das, D. M. (2026) · Integration · doi:10.1016/j.vlsi.2026.102805
Optoelectronic device geometries for maximizing photocurrent and flexibility
Kucia, P., & Rotenberg, M. Y. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103429
AI-integrated closed-loop brain-machine interfaces using soft bioelectronics
Kim, S., Kim, D., & Son, D. (2026) · Biomedical Engineering Letters · doi:10.1007/s13534-026-00606-8
An all-hydrogel fiber neural electrode array shrinks to the scale of neurons
Wu, S., Zhu, M., & Jiang, S. (2026) · Matter · doi:10.1016/j.matt.2026.102946
Needle-shaped Multichannel Recording Electrode for Spinal Dorsal Root Entry Zone (DREZ) Ablation
Pang, Y., Ma, D., Tang, X., Pang, Y., Wei, M., & Li, Z. (2026) · IEEE International Conference on Nano/Micro Engineered and Molecular Systems · doi:10.1109/nems69704.2026.11633689
Won, S., Boyadzhiev, A. M., Zhou, Q., 박준서, Fan, Y., Huang, S., Lim, T., & Zhang, H. (2026) · ACS Sensors · doi:10.1021/acssensors.6c02036
Fan, H., Jing, S., Zhang, Z., Yue, Y., Li, K., Li, Z., & Chen, S. (2026) · SmartMat · doi:10.1002/smm2.70107
In vivo magnetic recording of neuronal action potentials using a differential TMR magnetrode
Wang, Y., Shan, J., Zhang, C., Jin, Z., Song, Y., Cai, X., & Chen, J. (2026) · Microsystems & Nanoengineering · doi:10.1038/s41378-026-01421-y
Wu, S., & Sui, X. (2026) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-032-20287-1_49
Jo, H., Lee, G., Song, Y., Kim, S. Y., Kim, M., Manna, R., Choi, D., Aderibigbe, A. D., Suib, S. L., Park, K., et al. (2026) · Advanced Functional Materials · doi:10.1002/adfm.78133
Zheng, K., Yu, W., Gu, Y., He, Z., Wang, X., & Li, Y. (2026) · ACS Materials Letters · doi:10.1021/acsmaterialslett.6c00685
Dong, K., Song, H., Zhao, Q., Song, Z., Lv, F. Z., Wan, S., Zheng, T., Fan, Y., Liu, W.-C., Zhang, S., et al. (2026) · Science Advances · doi:10.1126/sciadv.aef7492
BioNICs: Biomaterial-enabled neural interfaces with closed-loop control for brain disorders
Ju, Y., Dou, Z., Yang, Y., & Yang, F. (2026) · EngMedicine · doi:10.1016/j.engmed.2026.100167
A wearable-to-implant wireless power transfer technology with variation tolerance
Nam, S., Seo, T., Yoo, S., Park, C., Kim, T., Yu, M., Kim, Y., Kang, H., Yeom, D., Cho, Y., et al. (2026) · Nature Electronics · doi:10.1038/s41928-026-01714-0
Soft and Transient Bioelectronics Enabled by Polymer‐Free Ti 3 C 2 T x MXene Hydrogels
Garg, R., Laimo, F. A., Ippolito, S., Upadhyay, P., Averbeck, S. R., Boufidis, D., Assenmacher, C., Adewole, D. O., Panigrahy, S. N., Mohammadlou, B. S., et al. (2026) · Advanced Materials · doi:10.1002/adma.75137
Toward adaptive cyborg tissues.
Li, Q., Lunshof, J., & Liu, J. (2026) · Trends in Biotechnology · doi:10.1016/j.tibtech.2026.09.009
A biohybrid mesh harvester for distributed energy harvesting in living tissues
Wang, S., Wang, X., Fu, S., Senger, C., Pfund, J. D., Jain, M., Katsumata, R., & Yao, J. (2026) · Science Advances · doi:10.1126/sciadv.aei5963
The Development and Evolution of Substrate Materials for Brain–Computer Interfaces
Ji, J., Gao, Y., Liu, J., Tian, M., & Wei, Y. (2026) · Polymers · doi:10.3390/polym18202569
Common phenomenal and neural substrate geometry in visual motion perception
Robinson, K., Zeleznikow-Johnston, A., Wu, J., Yoshimura, Y., & Tsuchiya, N. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122313 · Cited by 6
Conducting polymers for implantable neural electrodes in brain–computer interfaces
Zhu, J., Liu, R., Jiang, H., Liu, X., Kong, Z., Wang, L., Xu, W., & Wang, Y. (2026) · Journal of Materials Chemistry B · doi:10.1039/d6tb01636g
Reward coding across mouse visual cortical areas
Souza, J. P. G., Vieira, A. B., Ventura, V., de Lima, J. C., Arcanjo, G., de Paula, E. V., Sales, M., Souza, A. B. A., Alvarez-Jácobo, J. E., Fontenele, A. J., et al. (2026) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2026.1944695
Metadata
- Authors
- Hong, G., & Lieber, C. M.
- Affiliations
- Harvard University (US)
- Corresponding author
- Lieber, Charles M.
- Venue
- Nature Reviews Neuroscience
- Published
- 2019-03-04
- Volume
- 20
- Issue
- 6
- Pages
- 330-345
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- PMC
- PMC6531316
- OpenAlex
- W2920339115
- Cited by
- 695
- References
- 157
- Citations by year
- 2026: 58 · 2025: 104 · 2024: 101 · 2023: 117 · 2022: 108 · 2021: 110 · 2020: 77 · 2019: 19
- FWCI
- 10.45
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-04-16)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute on Aging (R00AG056636)
- National Institute of Biomedical Imaging and Bioengineering (DP1EB025835)
- Air Force Office of Scientific Research
- 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
- neural recording electrodes, single-unit recording, chronic neural recording, multifunctional electrodes, extracellular recording, neurotechnology
- MeSH terms
- Action Potentials, Animals, Brain, Electrodes, Electrodes, Implanted, Humans, Neurons
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
