Review Article | Published:

Novel electrode technologies for neural recordings

Nature Reviews Neuroscience (2019) | Download Citation


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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Galvani, L. De Viribus Electricitatis in Motu Musculari Commentarius [Italian] (Bologna Accademia delle Scienze, 1791).

  2. 2.

    von Helmholtz, H. Messungen über Fortpflanzungsgeschwindigkeit der Reizung in den Nerven [German]. Archiv Anat. Physiol. Wissenschaftliche Med. 19, 199–216 (1852).

  3. 3.

    Erlanger, J. & Gasser, H. S. Electrical Signs Of Nervous Activity (Humphrey Milford, Oxford Univ. Press, 1937).

  4. 4.

    Hodgkin, A. L. & Huxley, A. F. Action potentials recorded from inside a nerve fibre. Nature 144, 710–711 (1939).

  5. 5.

    Hubel, D. H. Tungsten microelectrode for recording from single units. Science 125, 549–550 (1957).

  6. 6.

    Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in cats visual cortex. J. Physiol. 160, 106–154 (1962).

  7. 7.

    McNaughton, B. L., O'Keefe, J. & Barnes, C. A. The stereotrode — a new technique for simultaneous isolation of several single units in the central nervous-system from multiple unit records. J. Neurosci. Methods 8, 391–397 (1983).

  8. 8.

    Wise, K. D., Angell, J. B. & Starr, A. An integrated-circuit approach to extracellular microelectrodes. IEEE Trans. Biomed. Eng. 17, 238–247 (1970).

  9. 9.

    Campbell, P. K., Jones, K. E. & Normann, R. A. A 100 electrode intracortical array: structural variability. Biomed. Sci. Instrum. 26, 161–165 (1990).

  10. 10.

    Buzsaki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420 (2012). This review article discusses the neurophysiological basis of different types of electrical signals in the brain and how these signals are measured with different electrode technologies.

  11. 11.

    Buzsaki, G. Theta oscillations in the hippocampus. Neuron 33, 325–340 (2002).

  12. 12.

    Yuste, R. From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16, 487–497 (2015). This historical overview summarizes the evolution of the fundamental neuroscience viewpoint from the neuron doctrine to neural network models with a focus on how this evolution has been fuelled by the emergence of multineuronal recording methods.

  13. 13.

    Harris, K. D., Quiroga, R. Q., Freeman, J. & Smith, S. L. Improving data quality in neuronal population recordings. Nat. Neurosci. 19, 1165–1174 (2016).

  14. 14.

    Carter, M. & Shieh, J. C. Guide to Research Techniques in Neuroscience (Academic Press, 2015).

  15. 15.

    Buzsaki, G. Large-scale recording of neuronal ensembles. Nat. Neurosci. 7, 446–451 (2004).

  16. 16.

    Rossant, C. et al. Spike sorting for large, dense electrode arrays. Nat. Neurosci. 19, 634–641 (2016).

  17. 17.

    Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

  18. 18.

    Keller, C. J., Chen, C., Lado, F. A. & Khodakhah, K. The limited utility of multiunit data in differentiating neuronal population activity. PLOS ONE 11, e0153154 (2016).

  19. 19.

    Ponce, C. R., Lomber, S. G. & Livingstone, M. S. Posterior inferotemporal cortex cells use multiple input pathways for shape encoding. J. Neurosci. 37, 5019–5034 (2017).

  20. 20.

    Gold, C., Henze, D. A., Koch, C. & Buzsaki, G. On the origin of the extracellular action potential waveform: a modeling study. J. Neurophysiol. 95, 3113–3128 (2006).

  21. 21.

    Harris, K. D., Henze, D. A., Csicsvari, J., Hirase, H. & Buzsaki, G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84, 401–414 (2000).

  22. 22.

    Hubel, D. H. & Wiesel, T. N. Receptive fields of single neurones in the cat's striate cortex. J. Physiol. 148, 574–591 (1959).

  23. 23.

    O’Keefe, J. & Dostrovsky, J. The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175 (1971).

  24. 24.

    Desimone, R., Albright, T. D., Gross, C. G. & Bruce, C. Stimulus-selective properties of inferior temporal neurons in the macaque. J. Neurosci. 4, 2051–2062 (1984).

  25. 25.

    Schultz, W. Responses of midbrain dopamine neurons to behavioral trigger stimuli in the monkey. J. Neurophysiol. 56, 1439–1461 (1986).

  26. 26.

    Newsome, W. T., Britten, K. H. & Movshon, J. A. Neuronal correlates of a perceptual decision. Nature 341, 52–54 (1989).

  27. 27.

    Hafting, T., Fyhn, M., Molden, S., Moser, M. B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005).

  28. 28.

    Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C. & Fried, I. Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005).

  29. 29.

    Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).

  30. 30.

    Lin, M. Z. & Schnitzer, M. J. Genetically encoded indicators of neuronal activity. Nat. Neurosci. 19, 1142–1153 (2016).

  31. 31.

    Poldrack, R. A. & Farah, M. J. Progress and challenges in probing the human brain. Nature 526, 371–379 (2015).

  32. 32.

    Yang, W. J. & Yuste, R. In vivo imaging of neural activity. Nat. Methods 14, 349–359 (2017).

  33. 33.

    Ji, N., Freeman, J. & Smith, S. L. Technologies for imaging neural activity in large volumes. Nat. Neurosci. 19, 1154–1164 (2016).

  34. 34.

    Deisseroth, K. Optogenetics: 10 years of microbial opsins in neuroscience. Nat. Neurosci. 18, 1213–1225 (2015).

  35. 35.

    Jorgenson, L. A. et al. The BRAIN Initiative: developing technology to catalyse neuroscience discovery. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140164 (2015).

  36. 36.

    Reardon, S. Worldwide brain-mapping project sparks excitement — and concern. Nature 537, 597 (2016).

  37. 37.

    Grinvald, A. & Hildesheim, R. VSDI: a new era in functional imaging of cortical dynamics. Nat. Rev. Neurosci. 5, 874–885 (2004).

  38. 38.

    Khan, H. N., Hounshell, D. A. & Fuchs, E. R. Science and research policy at the end of Moore’s law. Nat. Electron. 1, 14–21 (2018).

  39. 39.

    Stevenson, I. H. & Kording, K. P. How advances in neural recording affect data analysis. Nat. Neurosci. 14, 139–142 (2011).

  40. 40.

    Chen, R., Canales, A. & Anikeeva, P. Neural recording and modulation technologies. Nat. Rev. Mater. 2, 16093 (2017). This comprehensive review article focuses on recent materials-driven progress in neural probes.

  41. 41.

    Salatino, J. W., Ludwig, K. A., Kozai, T. D. Y. & Purcell, E. K. Glial responses to implanted electrodes in the brain. Nat. Biomed. Eng. 1, 862–877 (2017). This timely review article highlights the latest evidence on the role of glial cells in neural circuits. On the basis of this evidence, it also provides insights on the design of implanted brain probes from the perspective of the mechanical properties of the probe materials and the glial response in the neural tissue.

  42. 42.

    Eroglu, C. & Barres, B. A. Regulation of synaptic connectivity by glia. Nature 468, 223–231 (2010).

  43. 43.

    Clarke, L. E. & Barres, B. A. Emerging roles of astrocytes in neural circuit development. Nat. Rev. Neurosci. 14, 311–321 (2013).

  44. 44.

    Neves, H. P. et al. The NeuroProbes project: a concept for electronic depth control. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008, 1857 (2008).

  45. 45.

    Torfs, T. et al. Two-dimensional multi-channel neural probes with electronic depth control. IEEE Trans. Biomed. Circuits Syst. 5, 403–412 (2011).

  46. 46.

    Seidl, K. et al. CMOS-based high-density silicon microprobe arrays for electronic depth control in intracortical neural recording-characterization and application. J. Microelectromech. Syst. 21, 1426–1435 (2012).

  47. 47.

    Cheng, M. Y. et al. 3D probe array integrated with a front-end 100-channel neural recording ASIC. J. Micromech. Microeng. 24, 125010 (2014).

  48. 48.

    Fiath, R. et al. Large-scale recording of thalamocortical circuits: in vivo electrophysiology with the two-dimensional electronic depth control silicon probe. J. Neurophysiol. 116, 2312–2330 (2016).

  49. 49.

    Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017). This work demonstrates highly multiplexed integration of 960 recording electrodes into the same Michigan-type MEA.

  50. 50.

    Raducanu, B. C. et al. Time multiplexed active neural probe with 1356 parallel recording sites. Sensors (Basel) 17, 2388 (2017).

  51. 51.

    Rios, G., Lubenov, E. V., Chi, D., Roukes, M. L. & Siapas, A. G. Nanofabricated neural probes for dense 3D recordings of brain activity. Nano Lett. 16, 6857–6862 (2016).

  52. 52.

    Stringer, C. et al. Spontaneous behaviors drive multidimensional, brain-wide population activity. Preprint at bioRxiv (2018).

  53. 53.

    Angotzi, G. N. et al. A synchronous neural recording platform for multiple high-resolution CMOS probes and passive electrode arrays. IEEE Trans. Biomed. Circuits Syst. 12, 532–542 (2018).

  54. 54.

    Steinmetz, N. A., Koch, C., Harris, K. D. & Carandini, M. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Curr. Opin. Neurobiol. 50, 92–100 (2018).

  55. 55.

    Chung, J. E. et al. High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays. Neuron 101, 21–31 (2019).

  56. 56.

    Feiner, R. & Dvir, T. Tissue-electronics interfaces: from implantable devices to engineered tissues. Nat. Rev. Mater. 3, 17076 (2018). This review discusses the design principles for implantable electronic devices to interface with biological tissue and applications in electrophysiology and tissue engineering.

  57. 57.

    Viventi, J. et al. Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo. Nat. Neurosci 14, 1599–1605 (2011).

  58. 58.

    Khodagholy, D. et al. NeuroGrid: recording action potentials from the surface of the brain. Nat. Neurosci. 18, 310–315 (2015). This article provides the first demonstration of single-unit electrophysiology from the cortical surface with an ECoG array.

  59. 59.

    Gao, P. et al. A theory of multineuronal dimensionality, dynamics and measurement. Preprint at bioRxiv (2017).

  60. 60.

    Dickey, A. S., Suminski, A., Amit, Y. & Hatsopoulos, N. G. Single-unit stability using chronically implanted multielectrode arrays. J. Neurophysiol. 102, 1331–1339 (2009).

  61. 61.

    Vetter, R. J., Williams, J. C., Hetke, J. F., Nunamaker, E. A. & Kipke, D. R. Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex. IEEE Trans. Biomed. Eng. 51, 896–904 (2004).

  62. 62.

    Khodagholy, D., Gelinas, J. N. & Buzsaki, G. Learning-enhanced coupling between ripple oscillations in association cortices and hippocampus. Science 358, 369–372 (2017).

  63. 63.

    Avena-Koenigsberger, A., Misic, B. & Sporns, O. Communication dynamics in complex brain networks. Nat. Rev. Neurosci. 19, 17–33 (2018).

  64. 64.

    Frankland, P. W. & Bontempi, B. The organization of recent and remote memories. Nat. Rev. Neurosci. 6, 119–130 (2005).

  65. 65.

    Marblestone, A. H. et al. Physical principles for scalable neural recording. Front. Comput. Neurosci. 7, 137 (2013).

  66. 66.

    Welkenhuysen, M. et al. An integrated multi-electrode-optrode array for in vitro optogenetics. Sci. Rep. 6, 20353 (2016).

  67. 67.

    Zhang, A. Q. & Lieber, C. M. Nano-bioelectronics. Chem. Rev. 116, 215–257 (2016).

  68. 68.

    Kruskal, P. B., Jiang, Z., Gao, T. & Lieber, C. M. Beyond the patch clamp: nanotechnologies for intracellular recording. Neuron 86, 21–24 (2015).

  69. 69.

    Patolsky, F. et al. Detection, stimulation, and inhibition of neuronal signals with high-density nanowire transistor arrays. Science 313, 1100–1104 (2006).

  70. 70.

    Tian, B. Z. et al. Three-dimensional, flexible nanoscale field-effect transistors as localized bioprobes. Science 329, 830–834 (2010).

  71. 71.

    Parameswaran, R. & Tian, B. Z. Rational design of semiconductor nanostructures for functional subcellular interfaces. Acc. Chem. Res. 51, 1014–1022 (2018).

  72. 72.

    Khodagholy, D. et al. In vivo recordings of brain activity using organic transistors. Nat. Commun. 4, 1575 (2013).

  73. 73.

    Benfenati, V. et al. A transparent organic transistor structure for bidirectional stimulation and recording of primary neurons. Nat. Mater. 12, 672–680 (2013).

  74. 74.

    Cohen-Karni, T. et al. Synthetically encoded ultrashort-channel nanowire transistors for fast, pointlike cellular signal detection. Nano Lett. 12, 2639–2644 (2012).

  75. 75.

    Hong, G. S., Antaris, A. L. & Dai, H. J. Near-infrared fluorophores for biomedical imaging. Nat. Biomed. Eng. 1, 0010 (2017).

  76. 76.

    Hoover, E. E. & Squier, J. A. Advances in multiphoton microscopy technology. Nat. Photonics 7, 93–101 (2013).

  77. 77.

    Hochbaum, D. R. et al. All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins. Nat. Methods 11, 825–833 (2014).

  78. 78.

    Herry, C. & Johansen, J. P. Encoding of fear learning and memory in distributed neuronal circuits. Nat. Neurosci. 17, 1644–1654 (2014).

  79. 79.

    Jackson, A. & Fetz, E. E. Compact movable microwire array for long-term chronic unit recording in cerebral cortex of primates. J. Neurophysiol. 98, 3109–3118 (2007).

  80. 80.

    Chestek, C. A. et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J. Neural Eng. 8, 045005 (2011).

  81. 81.

    Liu, J. et al. Syringe-injectable electronics. Nat. Nanotechnol. 10, 629–636 (2015).

  82. 82.

    Xie, C. et al. Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes. Nat. Mater. 14, 1286–1292 (2015).

  83. 83.

    Fu, T.-M. et al. Stable long-term chronic brain mapping at the single-neuron level. Nat. Methods 13, 875–882 (2016). This work demonstrates chronically stable tracking of the same individual neurons over 8 months from the mouse brain with mesh electronics and presents a comprehensive set of rigorous metrics for single-unit-based analyses.

  84. 84.

    Zhou, T. et al. Syringe-injectable mesh electronics integrate seamlessly with minimal chronic immune response in the brain. Proc. Natl Acad. Sci. USA 114, 5894–5899 (2017).

  85. 85.

    Hong, G., Yang, X., Zhou, T. & Lieber, C. M. Mesh electronics: a new paradigm for tissue-like brain probes. Curr. Opin. Neurobiol. 50, 33–41 (2017).

  86. 86.

    Hong, G., Viveros, R. D., Zwang, T. J., Yang, X. & Lieber, C. M. Tissue-like neural probes for understanding and modulating the brain. Biochemistry 57, 3995–4004 (2018).

  87. 87.

    Fu, T.-M., Hong, G., Viveros, R. D., Zhou, T. & Lieber, C. M. Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology. Proc. Natl Acad. Sci. USA 114, E10046–E10055 (2017).

  88. 88.

    Park, S. et al. One-step optogenetics with multifunctional flexible polymer fibers. Nat. Neurosci. 20, 612–619 (2017).

  89. 89.

    Luan, L. et al. Ultraflexible nanoelectronic probes form reliable, glial scar-free neural integration. Sci. Adv. 3, e1601966 (2017).

  90. 90.

    Guitchounts, G., Markowitz, J. E., Liberti, W. A. & Gardner, T. J. A carbon-fiber electrode array for long-term neural recording. J. Neural Eng. 10, 046016 (2013).

  91. 91.

    Saxena, T. & Bellamkonda, R. V. Implantable electronics a sensor web for neurons. Nat. Mater. 14, 1190–1191 (2015).

  92. 92.

    Hong, G. S. et al. Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity. Nano Lett. 15, 6979–6984 (2015).

  93. 93.

    Schuhmann, T. G., Yao, J., Hong, G. S., Fu, T. M. & Lieber, C. M. Syringe-injectable electronics with a plug-and-play input/output interface. Nano Lett. 17, 5836–5842 (2017).

  94. 94.

    Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870–3896 (1998).

  95. 95.

    Bartho, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 92, 600–608 (2004).

  96. 96.

    Dzirasa, K., Fuentes, R., Kumar, S., Potes, J. M. & Nicolelis, M. A. L. Chronic in vivo multi-circuit neurophysiological recordings in mice. J. Neurosci. Methods 195, 36–46 (2011).

  97. 97.

    Kozai, T. D. Y. et al. Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces. Nat. Mater. 11, 1065–1073 (2012).

  98. 98.

    Canales, A. et al. Multifunctional fibers for simultaneous optical, electrical and chemical interrogation of neural circuits in vivo. Nat. Biotechnol. 33, 277–284 (2015). This work demonstrates incorporation of optical fibres, recording electrodes and microfluidic channels into the same multifunctional neural probe.

  99. 99.

    Wei, X. et al. Nanofabricated ultraflexible electrode arrays for high-density intracortical recording. Adv. Sci. 5, 1700625 (2018).

  100. 100.

    Morrison, J. H. & Baxter, M. G. The ageing cortical synapse: hallmarks and implications for cognitive decline. Nat. Rev. Neurosci. 13, 240–250 (2012).

  101. 101.

    Burke, S. N. & Barnes, C. A. Neural plasticity in the ageing brain. Nat. Rev. Neurosci. 7, 30–40 (2006).

  102. 102.

    Wang, M. et al. Neuronal basis of age-related working memory decline. Nature 476, 210–213 (2011).

  103. 103.

    Hong, G. et al. A method for single-neuron chronic recording from the retina in awake mice. Science 360, 1447–1451 (2018). This study uses mesh electronics to chronically track the activity of the same RGCs in mouse retina after non-surgical, intravitreal injection.

  104. 104.

    Service, R. F. Bioelectronics herald the rise of the cyborg. Science 358, 1233–1234 (2017).

  105. 105.

    Lien, A. D. & Scanziani, M. Cortical direction selectivity emerges at convergence of thalamic synapses. Nature 558, 80–86 (2018).

  106. 106.

    Zhao, Z. et al. Nanoelectronic coating enabled versatile multifunctional neural probes. Nano Lett. 17, 4588–4595 (2017).

  107. 107.

    Kandel, E. R. Principles of Neural Science (McGraw-Hill, 2013).

  108. 108.

    Grosenick, L., Marshel, J. H. & Deisseroth, K. Closed-loop and activity-guided optogenetic control. Neuron 86, 106–139 (2015).

  109. 109.

    Aston-Jones, G. & Deisseroth, K. Recent advances in optogenetics and pharmacogenetics. Brain Res. 1511, 1–5 (2013).

  110. 110.

    Bernal-Casas, D., Lee, H. J., Weitz, A. J. & Lee, J. H. Studying brain circuit function with dynamic causal modeling for optogenetic fMRI. Neuron 93, 522–532 (2017).

  111. 111.

    Canales, A., Park, S., Kilias, A. & Anikeeva, P. Multifunctional fibers as tools for neuroscience and neuroengineering. Acc. Chem. Res. 51, 829–838 (2018).

  112. 112.

    Miocinovic, S., Somayajula, S., Chitnis, S. & Vitek, J. L. History, applications, and mechanisms of deep brain stimulation. JAMA Neurol. 70, 163–171 (2013).

  113. 113.

    Borchers, S., Himmelbach, M., Logothetis, N. & Karnath, H. O. Direct electrical stimulation of human cortex — the gold standard for mapping brain functions? Nat. Rev. Neurosci. 13, 63–70 (2012).

  114. 114.

    Ashkan, K., Rogers, P., Bergman, H. & Ughratdar, I. Insights into the mechanisms of deep brain stimulation. Nat. Rev. Neurol. 13, 548–554 (2017).

  115. 115.

    Ramirez-Zamora, A. et al. Evolving applications, technological challenges and future opportunities in neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank. Front. Neurosci. 11, 734 (2018).

  116. 116.

    Cicchetti, F. & Barker, R. A. The glial response to intracerebrally delivered therapies for neurodegenerative disorders: is this a critical issue? Front. Pharmacol. 5, 139 (2014).

  117. 117.

    Neely, R. M., Piech, D. K., Santacruz, S. R., Maharbiz, M. M. & Carmena, J. M. Recent advances in neural dust: towards a neural interface platform. Curr. Opin. Neurobiol. 50, 64–71 (2018).

  118. 118.

    Seo, D. et al. Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron 91, 529–539 (2016).

  119. 119.

    Johnson, B. C. et al. in 2018 IEEE Custom Integrated Circuits Conf. (CICC) 1–4 (IEEE, 2018).

  120. 120.

    Packer, A. M., Roska, B. & Hausser, M. Targeting neurons and photons for optogenetics. Nat. Neurosci. 16, 805–815 (2013).

  121. 121.

    Stark, E., Koos, T. & Buzsaki, G. Diode probes for spatiotemporal optical control of multiple neurons in freely moving animals. J. Neurophysiol. 108, 349–363 (2012).

  122. 122.

    Wu, F. et al. Monolithically integrated µLEDs on silicon neural probes for high-resolution optogenetic studies in behaving animals. Neuron 88, 1136–1148 (2015).

  123. 123.

    Anikeeva, P. et al. Optetrode: a multichannel readout for optogenetic control in freely moving mice. Nat. Neurosci. 15, 163–170 (2012).

  124. 124.

    Royer, S. et al. Multi-array silicon probes with integrated optical fibers: light-assisted perturbation and recording of local neural circuits in the behaving animal. Eur. J. Neurosci. 31, 2279–2291 (2010).

  125. 125.

    Wang, J. et al. Integrated device for combined optical neuromodulation and electrical recording for chronic in vivo applications. J. Neural Eng. 9, 016001 (2012).

  126. 126.

    LeChasseur, Y. et al. A microprobe for parallel optical and electrical recordings from single neurons in vivo. Nat. Methods 8, 319–U363 (2011).

  127. 127.

    Lu, C. et al. Flexible and stretchable nanowire-coated fibers for optoelectronic probing of spinal cord circuits. Sci. Adv. 3, e1600955 (2017).

  128. 128.

    Kampasi, K. et al. Fiberless multicolor neural optoelectrode for in vivo circuit analysis. Sci. Rep. 6, 30961 (2016).

  129. 129.

    Son, Y. et al. In vivo optical modulation of neural signals using monolithically integrated two-dimensional neural probe arrays. Sci. Rep. 5, 15466 (2015).

  130. 130.

    Lee, J., Ozden, I., Song, Y. K. & Nurmikko, A. V. Transparent intracortical microprobe array for simultaneous spatiotemporal optical stimulation and multichannel electrical recording. Nat. Methods 12, 1157–1162 (2015).

  131. 131.

    Lee, H. J. et al. A multichannel neural probe with embedded microfluidic channels for simultaneous in vivo neural recording and drug delivery. Lab Chip 15, 1590–1597 (2015).

  132. 132.

    Shin, H. et al. Neural probes with multi-drug delivery capability. Lab Chip 15, 3730–3737 (2015).

  133. 133.

    Zrenner, C., Belardinelli, P., Muller-Dahlhaus, F. & Ziemann, U. Closed-loop neuroscience and non-invasive brain stimulation: a tale of two loops. Front. Cell. Neurosci. 10, 92 (2016).

  134. 134.

    Cui, Y., Wei, Q. Q., Park, H. K. & Lieber, C. M. Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species. Science 293, 1289–1292 (2001).

  135. 135.

    Kim, T. I. et al. Injectable, cellular-scale optoelectronics with applications for wireless optogenetics. Science 340, 211–216 (2013).

  136. 136.

    Gossler, C. et al. GaN-based micro-LED arrays on flexible substrates for optical cochlear implants. J. Phys. D Appl. Phys. 47, 205401 (2014).

  137. 137.

    Klein, E., Gossler, C., Paul, O. & Ruther, P. High-density µLED-based optical cochlear implant with improved thermomechanical behavior. Front. Neurosci. 12, 659 (2018).

  138. 138.

    Park, S. I. et al. Soft, stretchable, fully implantable miniaturized optoelectronic systems for wireless optogenetics. Nat. Biotechnol. 33, 1280–1286 (2015).

  139. 139.

    Montgomery, K. L. et al. Wirelessly powered, fully internal optogenetics for brain, spinal and peripheral circuits in mice. Nat. Methods 12, 969–974 (2015).

  140. 140.

    Geva-Sagiv, M., Las, L., Yovel, Y. & Ulanovsky, N. Spatial cognition in bats and rats: from sensory acquisition to multiscale maps and navigation. Nat. Rev. Neurosci. 16, 94–108 (2015).

  141. 141.

    Moser, E. I., Moser, M. B. & McNaughton, B. L. Spatial representation in the hippocampal formation: a history. Nat. Neurosci. 20, 1448–1464 (2017).

  142. 142.

    Hardcastle, K., Ganguli, S. & Giocomo, L. M. Cell types for our sense of location: where we are and where we are going. Nat. Neurosci. 20, 1474–1482 (2017).

  143. 143.

    Dai, X. C., Hong, G. S., Gao, T. & Lieber, C. M. Mesh nanoelectronics: seamless integration of electronics with tissues. Acc. Chem. Res. 51, 309–318 (2018).

  144. 144.

    Tian, B. Z. et al. Macroporous nanowire nanoelectronic scaffolds for synthetic tissues. Nat. Mater. 11, 986–994 (2012).

  145. 145.

    Scholvin, J. et al. Close-packed silicon microelectrodes for scalable spatially oversampled neural recording. IEEE Trans. Biomed. Eng. 63, 120–130 (2016).

  146. 146.

    Qing, Q. et al. Nanowire transistor arrays for mapping neural circuits in acute brain slices. Proc. Natl Acad. Sci. USA 107, 1882–1887 (2010).

  147. 147.

    Lacour, S. P., Courtine, G. & Guck, J. Materials and technologies for soft implantable neuroprostheses. Nat. Rev. Mater. 1, 16063 (2016). This review highlights the importance of minimizing the physical and mechanical mismatch between neural tissues and implantable electrodes from a perspective of neuroprosthetics.

  148. 148.

    Lee, H., Bellamkonda, R. V., Sun, W. & Levenston, M. E. Biomechanical analysis of silicon microelectrode-induced strain in the brain. J. Neural Eng. 2, 81–89 (2005).

  149. 149.

    Schwarz, D. A. et al. Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nat. Methods 11, 670–676 (2014).

  150. 150.

    Rousche, P. J. et al. Flexible polyimide-based intracortical electrode arrays with bioactive capability. IEEE Trans. Biomed. Eng. 48, 361–371 (2001).

  151. 151.

    Minev, I. R. et al. Electronic dura mater for long-term multimodal neural interfaces. Science 347, 159–163 (2015).

  152. 152.

    Tyler, W. J. The mechanobiology of brain function. Nat. Rev. Neurosci. 13, 867–878 (2012).

  153. 153.

    Polikov, V. S., Tresco, P. A. & Reichert, W. M. Response of brain tissue to chronically implanted neural electrodes. J. Neurosci. Methods 148, 1–18 (2005).

  154. 154.

    Henze, D. A. et al. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84, 390–400 (2000).

  155. 155.

    Schmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K. & Redish, A. D. Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience 131, 1–11 (2005).

  156. 156.

    Schmitzer-Torbert, N. & Redish, A. D. Neuronal activity in the rodent dorsal striatum in sequential navigation: separation of spatial and reward responses on the multiple T task. J. Neurophysiol. 91, 2259–2272 (2004).

  157. 157.

    Gray, C. M., Maldonado, P. E., Wilson, M. & McNaughton, B. Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex. J. Neurosci. Methods 63, 43–54 (1995).

  158. 158.

    Jog, M. S. et al. Tetrode technology: advances in implantable hardware, neuroimaging, and data analysis techniques. J. Neurosci. Methods 117, 141–152 (2002).

  159. 159.

    Insanally, M. et al. A low-cost, multiplexed µECoG system for high-density recordings in freely moving rodents. J. Neural Eng. 13, 026030 (2016).

Download references


The authors thank S. R. Patel for helpful discussions. C.M.L. acknowledges support of this work by the US Air Force Office of Scientific Research (FA9550-14-1-0136) and a US National Institutes of Health Director’s Pioneer Award (1DP1EB025835-01). G.H. acknowledges support of this work by the Pathway to Independence Award (Parent K99/R00) from the US National Institute on Aging of the National Institutes of Health (4R00AG056636-03).

Reviewer information

Nature Reviews Neuroscience thanks J. Robinson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information


  1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    • Guosong Hong
    •  & Charles M. Lieber
  2. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

    • Guosong Hong
  3. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    • Guosong Hong
  4. Center for Brain Science, Harvard University, Cambridge, MA, USA

    • Charles M. Lieber
  5. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    • Charles M. Lieber


  1. Search for Guosong Hong in:

  2. Search for Charles M. Lieber in:


Both authors researched data for the article, made substantial contributions to discussion of the content, wrote the article and reviewed or edited the manuscript before submission.

Competing interests

C.M.L. and G.H. are co-inventors on patents and patent applications relating to the article that have been filed by the authors’ institution (Harvard University), described below. The authors are not involved in efforts related to commercialization of this intellectual property (IP), including start-up companies or working with another company or group that might license the IP. ‘Scaffolds comprising nanoelectronic components, tissues, and other applications’, inventors C.M.L., J. Liu, B. Tian, T. Dvir, R. S. Langer and D. S. Kohane; US9,457,128 (issued); describes nanoscale transistors for cell recording. ‘Systems and methods for injectable devices’, inventors C.M.L., J. Liu, Z. Cheng, G.H., T.-M. Fu and T. Zhou; 61/975,601 (pending), PCT/US2015/024252 (pending) and 15/301,792 (pending); describes injectable mesh electronics. ‘Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity’, inventors C.M.L., G.H., T.-M. Fu and J. Huang; 62/201,006 (expired); describes injection method of mesh electronics. ‘Techniques and systems for injection and/or connection of electrical devices’, inventors C.M.L., G.H., T.-M. Fu and J. Huang; 62/209,255 (pending), PCT/US2016/045587 (issued) and 15/749,617 (pending); describes injection method of mesh electronics. ‘Interfaces for syringe-injectable electronics’, inventors C.M.L., T. G. Schuhmann, J. Yao, G.H. and T.-M. Fu; 62/505,562 (pending); describes the electrical connection method of mesh electronics.

Corresponding author

Correspondence to Charles M. Lieber.

Supplementary information



Microelectrode arrays each consisting of four closely bundled and independently addressable microwire electrodes. Action potentials from the same neuron can be detected with varying amplitudes across all four electrodes.

Michigan-type microelectrode arrays

Microelectrode arrays with recording sites distributed along the length of each silicon shank.

Utah-type microelectrode arrays

Microelectrode arrays of silicon shanks (usually 10 × 10) with recording sites located only at the tip of each silicon shank.

Local field potentials

(LFPs). Extracellular electric field oscillations in the 0–100 Hz frequency band that mainly arise from collective transmembrane ionic currents and act as a medium for network-level communication.

Fourier transform

A mathematical method that decomposes a waveform (for example, the neural recording trace), which is usually a function of time, into the frequencies that make it up.


A visual representation of frequencies of time-varying neural signals in a spectrum. A spectrogram is generated after performing Fourier transform of the neural recording trace.


Semiconductor devices that can be used to switch or amplify electronic signals and thus act as basic building blocks of integrated circuits in modern electronic devices.


Effective resistance of an electrical circuit to current when an oscillating voltage is applied. It has the same Ohm units as resistance but applies to AC circuits.


Separate paths in neural probes in which electrical, optical and chemical signals can flow to measure or modulate neural activity in an independently addressable manner.


Number of independent channels in a given neural probe. The multiplexity of a neural probe represents the information throughput at the brain–probe interface.

Complementary metal–oxide–semiconductor

(CMOS). A technology with design style that uses complementary pairs of p-type and n-type metal–oxide–semiconductor field-effect transistors for low-energy-cost logical functions in integrated circuits.

Young’s modulus

An intrinsic material property that measures the ability of a material to resist its change (increase or decrease) in length under tensile or compressive forces.


Fast and synchronous oscillatory pattern of the local field potential in the CA1 pyramidal layer of the hippocampus, usually in the frequency range of 100–200 Hz.

Photovoltaic effect

A process that generates voltage or electric current in a material upon exposure to light. Not to be confused with photoelectric effect, in which electrons are ejected from the material.

Field-effect transistors

(FETs). Three-terminal semiconductor devices in which the current flow (output) between the source and drain electrodes is modulated by the voltage (input) at the gate electrode.

Bending stiffness

The measure of the ability of a structure to resist bending deformation to applied force. It depends on the Young’s modulus of the material and the geometrical features of the structure.

Spike train cross-correlations

Pairwise comparisons of spike trains that reveal the latency in firing activity and potential monosynaptic excitatory or inhibitory interactions between two neurons.

Principal component analysis

(PCA). A statistical procedure to reduce the dimensionality and extract features of the extracellular spike waveform, thus assigning spikes to different neurons on the basis of waveform difference.

Thermal drawing process

A process commonly used in optical fibre production whereby macroscopic materials are heated and stretched to wires and sheets with greatly reduced thickness.

Closed-loop feedback

Electrical, optical and biochemical input to the nervous system that is adjusted by and used to control the measured output of neural activity.

Photonic integrated circuits

Optical counterparts of electronic integrated circuits, in which multiple photonic devices with distinct functions are integrated in the same circuitry.

Deep reactive ion etching

(DRIE). A type of reactive ion etching process used to create sharp-edged holes and trenches in the substrate usually with high anisotropy and high aspect ratio.


The acute episode during which an epileptic seizure occurs. The ictal period features abnormal bursts of electrical impulses in the brain and loss of consciousness.

About this article

Publication history