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Multiplexed, High Density Electrophysiology with Nanofabricated Neural Probes
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Extracellular electrode arrays can reveal the neuronal network correlates of behavior with single-cell, single-spike, and sub-millisecond resolution. However, implantable electrodes are inherently invasive, and efforts to scale up the number and density of recording sites must compromise on device size in order to connect the electrodes. Here, we report on silicon-based neural probes employing nanofabricated, high-density electrical leads. Furthermore, we address the challenge of reading out multichannel data with an application-specific integrated circuit (ASIC) performing signal amplification, band-pass filtering, and multiplexing functions. We demonstrate high spatial resolution extracellular measurements with a fully integrated, low noise 64-channel system weighing just 330 mg. The on-chip multiplexers make possible recordings with substantially fewer external wires than the number of input channels. By combining nanofabricated probes with ASICs we have implemented a system for performing large-scale, high-density electrophysiology in small, freely behaving animals that is both minimally invasive and highly scalable.
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“Some neural interface devices have been fabricated with high electrode densities; however, their designs have limited the use of these arrays to specific central nervous system structures, such as cortical grey matter (6.25 electrodes/mm 2 [12,13]; >150 electrodes/mm 2 [17,18]) or hippocampal slices (<11 electrodes/mm 2 [19]).”
From A new high-density (25 electrodes/mm2) penetrating microelectrode array for recording and stimulating sub-millimeter neuroanatomical structures (2013) · cites this paper as [17]
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Cites
Receptive 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
Theta oscillations in the hippocampus
Buzsáki, G. (2002) · Neuron · doi:10.1016/s0896-6273(02)00586-x · Cited by 3,332
Large-scale recording of neuronal ensembles
Buzsáki, G. (2004) · Nature Neuroscience · doi:10.1038/nn1233 · Cited by 1,875
Modulation of visual responses by behavioral state in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.033 · Cited by 1,483 · cited 1×
“For example, silicon probes containing dozens of recording sites on a thin penetrating shaft have yielded important insights into the function of the hippocampus [12] and visual cortex [13,14].”
BackgroundIntracellular Features Predicted by Extracellular Recordings in the Hippocampus In Vivo
Henze, D. A., Borhegyi, Z., Csicsvari, J., Mamiya, A., Harris, K. D., & Buzsáki, G. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.1.390 · Cited by 942
Barthó, P., Hirase, H., Monconduit, L., Zugaro, M., Harris, K. D., & Buzsáki, G. (2004) · Journal of Neurophysiology · doi:10.1152/jn.01170.2003 · Cited by 912
Organization of cell assemblies in the hippocampus
Harris, K. D., Csicsvari, J., Hirase, H., Dragoi, G., & Buzsáki, G. (2003) · Nature · doi:10.1038/nature01834 · Cited by 901
Multiple neural spike train data analysis: state-of-the-art and future challenges
Brown, E. N., Kass, R. E., & Mitra, P. P. (2004) · Nature Neuroscience · doi:10.1038/nn1228 · Cited by 866
Tungsten Microelectrode for Recording from Single Units
Hubel, D. H. (1957) · Science · doi:10.1126/science.125.3247.549 · Cited by 864
Brain responses to micro-machined silicon devices
Szarowski, D. H., Andersen, M. D., Retterer, S. T., Spence, A. J., Isaacson, M. S., Craighead, H. G., Turner, J. N., & Shain, W. G. (2003) · Brain Research · doi:10.1016/s0006-8993(03)03023-3 · Cited by 811
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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
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
Spike sorting for large, dense electrode arrays
Rossant, C., Kadir, S., Goodman, D. F. M., Schulman, J., Hunter, M. L. D., Saleem, A. B., Grosmark, A., Belluscio, M., Denfield, G. H., Ecker, A. S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4268 · Cited by 800
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
A Fully Automated Approach to Spike Sorting
Chung, J. E., Magland, J. F., Barnett, A. H., Tolosa, V., Tooker, A., Lee, K. Y., Shah, K. G., Felix, S. H., Frank, L. M., & Greengard, L. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.030 · Cited by 506
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
Self-citationChallenges and opportunities for large-scale electrophysiology with Neuropixels probes
Steinmetz, N. A., Koch, C., Harris, K. D., & Carandini, M. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.01.009 · Cited by 356
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
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
Consciousness in humans and non-human animals: recent advances and future directions
Boly, M., Seth, A. K., Wilke, M., Ingmundson, P., Baars, B., Laureys, S., Edelman, D. B., & Tsuchiya, N. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00625 · Cited by 291
An Implantable 455-Active-Electrode 52-Channel CMOS Neural Probe
Lopez, C. M., Andrei, A., Mitra, S., Welkenhuysen, M., Eberle, W., Bartic, C., Puers, R., Yazicioglu, R. F., & Gielen, G. G. E. (2014) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2013.2284347 · Cited by 264
Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1
Dadarlat, M. C., & Stryker, M. P. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.2728-16.2017 · Cited by 205
Close-Packed Silicon Microelectrodes for Scalable Spatially Oversampled Neural Recording
Scholvin, J., Kinney, J. P., Bernstein, J. G., Moore-Kochlacs, C., Kopell, N., Fonstad, C. G., & Boyden, E. S. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2406113 · Cited by 203
Panier, T., Romano, S. A., Olive, R., Pietri, T., Sumbre, G., Candelier, R., & Debrégeas, G. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00065 · Cited by 225
A Neural Probe With Up to 966 Electrodes and Up to 384 Configurable Channels in 0.13 $\mu$m SOI CMOS
López, C. M., Putzeys, J., Raducanu, B. C., Ballini, M., Wang, S., Andrei, A., Rochus, V., Vandebriel, R., Severi, S., Hoof, C. A. V., et al. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2016.2646901 · Cited by 198
Reconfigurable nanophotonic silicon probes for sub-millisecond deep-brain optical stimulation
Mohanty, A., Li, Q., Tadayon, M. A., Roberts, S. P., Bhatt, G. R., Shim, E., Ji, X., Cardenas, J., Miller, S. A., Kepecs, Á., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0516-y · Cited by 198
Flexible, Penetrating Brain Probes Enabled by Advances in Polymer Microfabrication
Weltman, A., Yoo, J. J., & Meng, E. F. (2016) · Micromachines · doi:10.3390/mi7100180 · Cited by 192
Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfaces
Seo, D., Carmena, J. M., Rabaey, J. M., Alon, E., & Maharbiz, M. M. (2013) · arXiv · doi:10.48550/arxiv.1307.2196 · Cited by 172
Shin, H., Son, Y., Chae, U., Kim, J., Choi, N., Lee, H. J., Woo, J., Cho, Y., Yang, S. H., Lee, C. J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11628-5 · Cited by 174
Implantable aptamer–field-effect transistor neuroprobes for in vivo neurotransmitter monitoring
Zhao, C., Cheung, K. M., Huang, I.-W., Yang, H., Nakatsuka, N., Liu, W., Cao, Y. F., Man, T., Weiss, P. S., Monbouquette, H. G., et al. (2021) · Science Advances · doi:10.1126/sciadv.abj7422 · Cited by 171
Time Multiplexed Active Neural Probe with 1356 Parallel Recording Sites
Raducanu, B. C., Yazıcıoğlu, R. F., López, C. M., Ballini, M., Putzeys, J., Wang, S., Andrei, A., Rochus, V., Welkenhuysen, M., Helleputte, N. V., et al. (2017) · Sensors · doi:10.3390/s17102388 · Cited by 166
Patel, P. R., Na, K., Zhang, H., Kozai, T. D. Y., Kotov, N. A., Yoon, E., & Chestek, C. A. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046009 · Cited by 165
Automated long-term recording and analysis of neural activity in behaving animals
Dhawale, A. K., Poddar, R., Wolff, S. B., Normand, V. A., Kopelowitz, E., & Ölveczky, B. P. (2017) · eLife · doi:10.7554/elife.27702 · Cited by 159
Model validation of untethered, ultrasonic neural dust motes for cortical recording
Seo, D., Carmena, J. M., Rabaey, J. M., Maharbiz, M. M., & Alon, E. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.07.025 · Cited by 158
Nanofabricated Neural Probes for Dense 3-D Recordings of Brain Activity
Rios, G., Lubenov, E. V., Chi, D., Roukes, M. L., & Siapas, A. G. (2016) · Nano Letters · doi:10.1021/acs.nanolett.6b02673 · Cited by 157
Luan, L., Robinson, J. T., Aazhang, B., Chi, T., Yang, K., Li, X., Rathore, H., Singer, A., Yellapantula, S., Fan, Y., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.011 · Cited by 150
A very large-scale microelectrode array for cellular-resolution electrophysiology
Tsai, D., Sawyer, D., Bradd, A., Yuste, R., & Shepard, K. L. (2017) · Nature Communications · doi:10.1038/s41467-017-02009-x · Cited by 150
Wark, H. A. C., Sharma, R., Mathews, K. S., Fernandez, E., Yoo, J., Christensen, B., Tresco, P., Rieth, L., Solzbacher, F., Normann, R. A., et al. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/045003 · Cited by 148
Brain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes
Shobe, J. L., Claar, L. D., Parhami, S., Bakhurin, K. I., & Masmanidis, S. C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00464.2015 · Cited by 152
Self-citationNanofabricated Ultraflexible Electrode Arrays for High‐Density Intracortical Recording
Wei, X., Luan, L., Zhao, Z., Li, X., Zhu, H., Potnis, O., & Xie, C. (2018) · Advanced Science · doi:10.1002/advs.201700625 · Cited by 149
Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential
Łęski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003137 · Cited by 149
Optimal Electrode Size for Multi-Scale Extracellular-Potential Recording From Neuronal Assemblies
Viswam, V., Obien, M. E. J., Franke, F., Frey, U., & Hierlemann, A. R. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00385 · Cited by 136
Nanostructures: a platform for brain repair and augmentation
Vidu, R., Rahman, M., Mahmoudi, M., Enăchescu, M., Potecă, T. D., & Opriș, I. C. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00091 · Cited by 140
A 75-µW, 16-Channel Neural Spike-Sorting Processor With Unsupervised Clustering
Karkare, V., Gibson, S., & Markovic, D. M. (2013) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2013.2264616 · Cited by 130
Lazaro, M. T., Taxidis, J., Shuman, T., Bachmutsky, I., Ikrar, T., Santos, R., Marcello, G. M., Mylavarapu, A., Chandra, S., Foreman, A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.05.006 · Cited by 125
Self-citationThe sifting of visual information in the superior colliculus
Lee, K. H., Tran, A., Turan, Z., & Meister, M. (2020) · eLife · doi:10.7554/elife.50678 · Cited by 120
Does Impedance Matter When Recording Spikes With Polytrodes?
Neto, J. P., Baião, P., Lopes, G., Frazão, J., Nogueira, J., Fortunato, E. M. C., Barquinha, P. M. C., & Kampff, A. R. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00715 · Cited by 103
Recent advances in silicon-based neural microelectrodes and microsystems: a review
Fekete, Z. (2015) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2015.03.055 · Cited by 99
Dorigo, D. D., Moranz, C., Graf, H., Marx, M., Wendler, D., Shui, B., Herbawi, A. S., Kuhl, M., Ruther, P., Paul, O., et al. (2018) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2018.2873180 · Cited by 94
Ito, S., Feldheim, D. A., & Litke, A. M. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3689-16.2017 · Cited by 92
Validating silicon polytrodes with paired juxtacellular recordings: method and dataset
Neto, J. P., Lopes, G., Frazão, J., Nogueira, J., Lacerda, P., Baião, P., Aarts, A. A. A., Andrei, A., Musa, S., Fortunato, E. M. C., et al. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00103.2016 · Cited by 91
Kirchgessner, M. A., Franklin, A. D., & Callaway, E. M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002080117 · Cited by 89
Open source silicon microprobes for high throughput neural recording
Yang, L., Lee, K., Villagracia, J., & Masmanidis, S. C. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab581a · Cited by 93
Self-citationSung, C., Jeon, W., Nam, K. S., Kim, Y., Butt, H., & Park, S. (2020) · Journal of Materials Chemistry B · doi:10.1039/d0tb00872a · Cited by 87
In vivo optogenetic identification and manipulation of GABAergic interneuron subtypes
Roux, L., Stark, E., Sjulson, L., & Buzsáki, G. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.12.013 · Cited by 84
Fiáth, R., Raducanu, B. C., Musa, S., Andrei, A., López, C. M., Hoof, C. A. V., Ruther, P., Aarts, A. A. A., Horváth, D., & Ulbert, I. (2018) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2018.01.060 · Cited by 81
Shim, E., Chen, Y., Masmanidis, S. C., & Li, M. (2016) · Scientific Reports · doi:10.1038/srep22693 · Cited by 80
Self-citationNewman, J. P., Zeller-Townson, R., Fong, M., Desai, S. A., Gross, R. E., & Potter, S. M. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00098 · Cited by 75
22.7 A 966-electrode neural probe with 384 configurable channels in 0.13µm SOI CMOS
López, C. M., Mitra, S., Putzeys, J., Raducanu, B., Ballini, M., Andrei, A., Severi, S., Welkenhuysen, M., Hoof, C. A. V., Musa, S., et al. (2016) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2016.7418072 · Cited by 68
Nanoelectronic Coating Enabled Versatile Multifunctional Neural Probes
Zhao, Z., Luan, L., Wei, X., Zhu, H., Li, X., Lin, S. Q., Siegel, J. J., Chitwood, R. A., & Xie, C. (2017) · Nano Letters · doi:10.1021/acs.nanolett.7b00956 · Cited by 65
Translational Neuroelectronics
Jastrzebska‐Perfect, P., Chowdhury, S., Spyropoulos, G. D., Zhao, Z., Cea, C., Gelinas, J. N., & Khodagholy, D. (2020) · Advanced Functional Materials · doi:10.1002/adfm.201909165 · Cited by 68
New approaches for CMOS-based devices for large-scale neural recording
Ruther, P., & Paul, O. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.10.007 · Cited by 61
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Zhao, C., Man, T., Cao, Y. F., Weiss, P. S., Monbouquette, H. G., & Andrews, A. M. (2022) · ACS Sensors · doi:10.1021/acssensors.2c01909 · Cited by 61
Nanowire electrodes for high-density stimulation and measurement of neural circuits
Robinson, J. T., Jorgolli, M., & Park, H. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00038 · Cited by 62
Neural Probes for Chronic Applications
Kook, G., Lee, S., Lee, H., Cho, I., & Lee, H. J. (2016) · Micromachines · doi:10.3390/mi7100179 · Cited by 61
Distinct “driving” versus “modulatory” influences of different visual corticothalamic pathways
Kirchgessner, M. A., Franklin, A. D., & Callaway, E. M. (2021) · Current Biology · doi:10.1016/j.cub.2021.09.025 · Cited by 59
Kim, H. W., Kim, J., Kim, J. Y., Kim, K., Lee, J. Y., Kim, T., Cho, S., An, J. B., Kim, H. J., Sun, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59303-2 · Cited by 52
Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology
Siegle, J. H., Hale, G., Newman, J. P., & Voigts, J. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.11.004 · Cited by 56
Coordinated neuronal ensembles in primary auditory cortical columns
See, J. Z., Atencio, C. A., Sohal, V. S., & Schreiner, C. E. (2018) · eLife · doi:10.7554/elife.35587 · Cited by 57
Time multiplexed active neural probe with 678 parallel recording sites
Raducanu, B. C., Yazıcıoğlu, R. F., López, C. M., Ballini, M., Putzeys, J., Wang, S., Andrei, A., Welkenhuysen, M., Helleputte, N. V., Musa, S., et al. (2016) · European Solid-State Device Research Conference · doi:10.1109/essderc.2016.7599667 · Cited by 51
Electrophysiology Read-Out Tools for Brain-on-Chip Biotechnology
Forró, C., Caron, D., Angotzi, G. N., Gallo, V., Berdondini, L., Santoro, F., Palazzolo, G., & Panuccio, G. (2021) · Micromachines · doi:10.3390/mi12020124 · Cited by 54
Emergence of Slow-Switching Assemblies in Structured Neuronal Networks
Schaub, M. T., Billeh, Y. N., Anastassiou, C. A., Koch, C., & Barahona, M. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004196 · Cited by 50
Seidl, K., Schwaerzle, M., Ulbert, I., Neves, H. P., Paul, O., & Ruther, P. (2012) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2012.2206564 · Cited by 47
Shanks, J. A., Ito, S., Schaevitz, L. R., Yamada, J., Chen, B., Litke, A. M., & Feldheim, D. A. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4599-15.2016 · Cited by 46
Divergence in Population Coding for Space between Dorsal and Ventral CA1
Chockanathan, U., & Padmanabhan, K. (2021) · eNeuro · doi:10.1523/eneuro.0211-21.2021 · Cited by 45
MANTA—an open-source, high density electrophysiology recording suite for MATLAB
Englitz, B., David, S. V., Sorenson, M. D., & Shamma, S. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00069 · Cited by 41
Opto‐electro‐thermal optimization of photonic probes for optogenetic neural stimulation
Dong, N., Palmini, R. B., Soltan, A., Ponon, N. K., O'Neil, A., Travelyan, A., Maaskant, P. P., Degenaar, P. A., & Sun, X. (2018) · Journal of Biophotonics · doi:10.1002/jbio.201700358 · Cited by 41
Inter-laminar microcircuits across neocortex: repair and augmentation
Opriș, I. C. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2013.00080 · Cited by 40
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
An implantable 455-active-electrode 52-channel CMOS neural probe
López, C. M., Andrei, A., Mitra, S., Welkenhuysen, M., Eberle, W., Bartic, C., Puers, R., Yazıcıoğlu, R. F., & Gielen, G. E. (2013) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2013.6487738 · Cited by 39
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
McBride, E. G., Lee, S.-Y. J., & Callaway, E. M. (2019) · Current Biology · doi:10.1016/j.cub.2019.03.065 · Cited by 39
Schwartz, Z. P., Buran, B. N., & David, S. V. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00595.2019 · Cited by 39
Biocompatible reference electrodes to enhance chronic electrochemical signal fidelity in vivo
Seaton, B. T., & Heien, M. L. (2021) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-021-03640-w · Cited by 37
Chik, G. K. K., Xiao, N., Ji, X., Tsang, A. C. O., Leung, G., Zhang, S., Tin, C., & Chan, P. K. L. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202200143 · Cited by 33
Fiáth, R., Beregszászi, P., Horváth, D., Wittner, L., Aarts, A. A. A., Ruther, P., Neves, H. P., Bokor, H., Acsády, L., & Ulbert, I. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00318.2016 · Cited by 32
High‐density implantable neural electrodes and chips for massive neural recordings
Wang, L., Suo, Y., Wang, J., Wang, X., Xue, K., An, J., Sun, X., Chen, Q., Tang, X., Zhao, Y., et al. (2024) · Brain‐X · doi:10.1002/brx2.65 · Cited by 31
CHIME: CMOS-Hosted in vivo Microelectrodes for Massively Scalable Neuronal Recordings
Köllő, M., Rácz, R., Hanna, M.-E., Obaid, A., Angle, M. R., Wray, W., Kong, Y., Hierlemann, A. R., Müller, J., Melosh, N. A., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00834 · Cited by 31
CMOS Neural Probe With 1600 Close-Packed Recording Sites and 32 Analog Output Channels
Herbawi, A. S., Christ, O., Kiessner, L., Mottaghi, S., Hofmann, U., Paul, O., & Ruther, P. (2018) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2018.2872619 · Cited by 30
New Perspectives on Neuroengineering and Neurotechnologies: NSF-DFG Workshop Report
Moritz, C. T., Ruther, P., Goering, S., Stett, A., Ball, T., Burgard, W., Chudler, E. H., & Rao, R. P. N. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2543662 · Cited by 30
Nano functional neural interfaces
Wang, Y., Zhu, H., Yang, H., Argall, A. D., Luan, L., Xie, C., & Guo, L. (2018) · Nano Research · doi:10.1007/s12274-018-2127-4 · Cited by 30
Zhao, Z., Gong, R., Zheng, L., & Wang, J. (2016) · Sensors · doi:10.3390/s16111851 · Cited by 29
Crosstalk in polymer microelectrode arrays
Qiang, Y., Gu, W., Liu, Z., Liang, S., Ryu, J. H., Seo, K. J., Liu, W., & Fang, H. (2021) · Nano Research · doi:10.1007/s12274-021-3442-8 · Cited by 29
Perna, A., Angotzi, G. N., Berdondini, L., & Ribeiro, J. F. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1275908 · Cited by 30
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
Chamanzar, M., Denman, D. J., Blanche, T. J., & Maharbiz, M. M. (2015) · IEEE International Conference on Micro Electro Mechanical Systems (MEMS) · doi:10.1109/memsys.2015.7051049 · Cited by 26
Self-citationMultilayer 3D electrodes for neural implants
Leccardi, M. J. I. A., Vagni, P., & Ghezzi, D. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae191 · Cited by 26
Jones, R. T., Barth, A. M., Ormiston, L. D., & Módy, I. (2015) · Epilepsia · doi:10.1111/epi.13218 · Cited by 25
A low-cost, scalable, current-sensing digital headstage for high channel count μ ECoG
Trumpis, M., Insanally, M. N., Zou, J., Elsharif, A., Ghomashchi, A., Artan, N. S., Froemke, R. C., & Viventi, J. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa5a82 · Cited by 26
Hoseini, M. S., Higashikubo, B. T., Cho, F. S., Chang, A. H., Clemente-Perez, A., Lew, I., Ciesielska, A., Stryker, M. P., & Paz, J. T. (2021) · eLife · doi:10.7554/elife.61437 · Cited by 25
Zhang, F., Aghagolzadeh, M., & Oweiss, K. G. (2012) · Journal of Signal Processing Systems · doi:10.1007/s11265-012-0670-x · Cited by 23
Ritter, L. M., Golshani, P., Takahashi, K. U., Dufour, S., Valiante, T. A., & Kokaia, M. (2014) · Epilepsia · doi:10.1111/epi.12804 · Cited by 23
Angotzi, G. N., Malerba, M., Boi, F., Miele, E., Maccione, A., Amin, H., Crepaldi, M., & Berdondini, L. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2792046 · Cited by 23
Statistically Reconstructed Multiplexing for Very Dense, High-Channel-Count Acquisition Systems
Tsai, D., Yuste, R., & Shepard, K. L. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2750484 · Cited by 23
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Li, B., Lee, K., Masmanidis, S. C., & Li, M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aabc94 · Cited by 23
Self-citationWhy not record from every electrode with a CMOS scanning probe?Preprint
Dimitriadis, G., Neto, J. P., Aarts, A. A. A., Andrei, A., Ballini, M., Battaglia, F. P., Calcaterra, L., Chen, S., David, F., Fiáth, R., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/275818 · Cited by 23
3D probe array integrated with a front-end 100-channel neural recording ASIC
Cheng, M., Yao, L., Tan, K. L., Lim, R., Li, P., & Chen, W. (2014) · Journal of Micromechanics and Microengineering · doi:10.1088/0960-1317/24/12/125010 · Cited by 20
Sim, S., Shin, H., Bae, K., Han, H., Kang, Y., Woo, J., Cho, Y., Cho, I., & Kim, J. (2023) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2023.134124 · Cited by 19
Variability of acute extracellular action potential measurements with multisite silicon probes
Scott, K. M., Du, J., Lester, H. A., & Masmanidis, S. C. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.08.005 · Cited by 19
Self-citationSecondary auditory cortex mediates a sensorimotor mechanism for action timing
Cook, J. R., Li, H., Nguyen, B., Huang, H.-H., Mahdavian, P., Kirchgessner, M. A., Strassmann, P., Engelhardt, M. D., Callaway, E. M., & Jin, X. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01025-5 · Cited by 22
Metadata
- Authors
- Du, J., Blanche, T. J., Harrison, R. R., Lester, H. A., & Masmanidis, S. C.
- Affiliations
- California Institute of Technology (US)
- University of California, Berkeley (US)
- Intan Technologies (United States) (US)
- Corresponding author
- Masmanidis, Sotiris C.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-10-12
- Volume
- 6
- Issue
- 10
- Pages
- e26204
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3192171
- OpenAlex
- W2081826483
- Cited by
- 231
- References
- 51
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 6 · 2023: 14 · 2022: 10 · 2021: 21 · 2020: 19 · 2019: 14 · 2018: 19 · 2017: 19 · 2016: 28 · 2015: 19 · 2014: 16 · 2013: 20 · 2012: 5
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Funders
- California Institute of Technology
- National Institutes of Health (5r21ns066260-02, DA017279)
- Kavli Nanoscience Institute, California Institute of Technology
- National Institute on Drug Abuse (R01DA017279, DA017279)
- National Institute of Neurological Disorders and Stroke (R21 NS066260, 5R21NS066260)
- 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 probes, extracellular recording, application-specific integrated circuits, silicon neural probe, implanted electrodes, freely moving animals
- MeSH terms
- Animals, Behavior, Animal, Electrodes, Male, Mice, Inbred C57BL, Neurons, Signal Processing, Computer-Assisted, Silicon, Temperature, Molecular Probes, Nanotechnology, Nanostructures, Mice, Electrophysiological Phenomena
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