[1] Reardon, S. Worldwide brain-mapping project sparks excitement—and concern. Nature 537, 597 (2016). doi: 10.1038/nature.2016.20658
[2] Schölkopf, B. Artificial intelligence. Learning to see and act. Nature 518, 486-487 (2015). doi: 10.1038/518486a
[3] Rutten, W. L. C. Selective electrical interfaces with the nervous system. Annu. Rev. Biomed. Eng. 4, 407-452 (2002). doi: 10.1146/annurev.bioeng.4.020702.153427
[4] Del Pozo-Banos, M. et al. Electroencephalogram subject identification: a review. Expert Syst. Appl. 41, 6537-6554 (2014). doi: 10.1016/j.eswa.2014.05.013
[5] Cox, R. W. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162-173 (1996). doi: 10.1006/cbmr.1996.0014
[6] Makela, T. et al. A review of cardiac image registration methods. IEEE Trans. Med. Imaging 21, 1011-1021 (2002). doi: 10.1109/TMI.2002.804441
[7] Berning, S. et al. Nanoscopy in a living mouse brain. Science 335, 551 (2012). doi: 10.1126/science.1215369
[8] Markram, H. The blue brain project. Nat. Rev. Neurosci. 7, 153-160 (2006). doi: 10.1038/nrn1848
[9] Hines, M. L. & Carnevale, N. T. The NEURON simulation environment. Neural Comput. 9, 1179-1209 (1997). doi: 10.1162/neco.1997.9.6.1179
[10] Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668-673 (2014). doi: 10.1126/science.1254642
[11] Schemmel, J. et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling. Proceedings of 2010 IEEE International Symposium on Circuits and Systems. Paris, France: IEEE, pp1947-1950 (2010).
[12] Psaltis, D. et al. Holography in artificial neural networks. Nature 343, 325-330 (1990). doi: 10.1038/343325a0
[13] Tait, A. N. et al. Broadcast and weight: an integrated network for scalable photonic spike processing. J. Light Technol. 32, 4029-4041 (2014). doi: 10.1109/JLT.2014.2345652
[14] Tait, A. N. et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep. 7, 7430 (2017). doi: 10.1038/s41598-017-07754-z
[15] Shen, Y. C. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11, 441-446 (2017). doi: 10.1038/nphoton.2017.93
[16] Mahoney, M. J. & Anseth, K. S. Three-dimensional growth and function of neural tissue in degradable polyethylene glycol hydrogels. Biomaterials 27, 2265-2274 (2006). doi: 10.1016/j.biomaterials.2005.11.007
[17] Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456-492 (2015). doi: 10.1016/j.cell.2015.09.029
[18] Zhang, K. W. et al. A fiber optic sensor for the measurement of surface roughness and displacement using artificial neural networks. IEEE Trans. Instrum. Meas. 46, 899-902 (1997). doi: 10.1109/19.650796
[19] Khan, J. et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7, 673-679 (2001). doi: 10.1038/89044
[20] Peurifoy, J. et al. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv. 4, eaar4206 (2018). doi: 10.1126/sciadv.aar4206
[21] Hell, S. W. & Wichmann, J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett. 19, 780-782 (1994). doi: 10.1364/OL.19.000780
[22] Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642-1645 (2006). doi: 10.1126/science.1127344
[23] Gan, Z. S. et al. Three-dimensional deep sub-diffraction optical beam lithography with 9 nm feature size. Nat. Commun. 4, 2061 (2013). doi: 10.1038/ncomms3061
[24] Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629-1636 (1990). doi: 10.1109/5.58356
[25] Uhrig, R. E. Introduction to artificial neural networks. Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics. Orlando, FL, USA: IEEE, pp 33-37 (1995).
[26] McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115-133 (1943). doi: 10.1007/BF02478259
[27] Hinton, G. Mental simulation. Nature 347, 627-628 (1990).
[28] Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533-536 (1986). doi: 10.1038/323533a0
[29] Froemke, R. C. & Dan, Y. Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416, 433-438 (2002). doi: 10.1038/416433a
[30] Hebb, D. O. The organization of behavior. in Neurocomputing: Foundations of Research (eds. Anderson, J. A. & Rosenfeld, E.) 43-54 (Cambridge, MA, USA: MIT Press, 1988).
[31] Chua, L. Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18, 507-519 (1971). doi: 10.1109/TCT.1971.1083337
[32] Strukov, D. B. et al. The missing memristor found. Nature 453, 80-83 (2008). doi: 10.1038/nature06932
[33] Williams, R. S. How we found the missing memristor. IEEE Spectr. 45, 28-35 (2008).
[34] Jo, S. H. et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297-1301 (2010). doi: 10.1021/nl904092h
[35] Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61-64 (2015). doi: 10.1038/nature14441
[36] Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114-117 (2013). doi: 10.1038/nmat3510
[37] Wang, Z. R. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137-145 (2018). doi: 10.1038/s41928-018-0023-2
[38] Geim, A. K. & Novoselov, K. S. The rise of graphene. Nat. Mater. 6, 183-191 (2007). doi: 10.1038/nmat1849
[39] El-Kady, M. F. & Kaner, R. B. Scalable fabrication of high-power graphene micro-supercapacitors for flexible and on-chip energy storage. Nat. Commun. 4, 1475 (2013). doi: 10.1038/ncomms2446
[40] Strong, V. et al. Patterning and electronic tuning of laser scribed graphene for flexible all-carbon devices. ACS Nano 6, 1395-1403 (2012). doi: 10.1021/nn204200w
[41] Tian, H. et al. Wafer-scale integration of graphene-based electronic, optoelectronic and electroacoustic devices. Sci. Rep. 4, 3598 (2014).
[42] Tian, H. et al. Cost-effective, transfer-free, flexible resistive random access memory using laser-scribed reduced graphene oxide patterning technology. Nano Lett. 14, 3214-3219 (2014).
[43] Zhao, F. et al. Functionalized graphitic carbon nitride for metal-free, flexible and rewritable nonvolatile memory device via direct laser-writing. Sci. Rep. 4, 5882 (2014).
[44] Deng, R. R. & Liu, X. G. Optical multiplexing: Tunable lifetime nanocrystals. Nat. Photonics 8, 10-12 (2014). doi: 10.1038/nphoton.2013.353
[45] Zijlstra, P., Chon, J. W. M. & Gu, M. Five-dimensional optical recording mediated by surface plasmons in gold nanorods. Nature 459, 410-413 (2009). doi: 10.1038/nature08053
[46] Li, X. P. et al. Athermally photoreduced graphene oxides for three-dimensional holographic images. Nat. Commun. 6, 6984 (2015). doi: 10.1038/ncomms7984
[47] Ren, H. R. et al. On-chip noninterference angular momentum multiplexing of broadband light. Science 352, 805-809 (2016). doi: 10.1126/science.aaf1112
[48] Deng, R. R. et al. Temporal full-colour tuning through non-steady-state upconversion. Nat. Nanotechnol. 10, 237-242 (2015). doi: 10.1038/nnano.2014.317
[49] Asghari, M. & Krishnamoorthy, A. V. Silicon photonics. Energy-efficient communication. Nat. Photonics 5, 268-270 (2011). doi: 10.1038/nphoton.2011.68
[50] Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011). doi: 10.1038/ncomms1476
[51] Mesaritakis, C. et al. Artificial neuron based on integrated semiconductor quantum dot mode-locked lasers. Sci. Rep. 6, 39317 (2016). doi: 10.1038/srep39317
[52] Rosenbluth, D. et al. A high performance photonic pulse processing device. Opt. Express 17, 22767-22772 (2009). doi: 10.1364/OE.17.022767
[53] Li, S. H. & Cai, X. H. High-contrast all optical bistable switching in coupled nonlinear photonic crystal microcavities. Appl. Phys. Lett. 96, 131114 (2010). doi: 10.1063/1.3378812
[54] Ríos, C. et al. Integrated all-photonic non-volatile multi-level memory. Nat. Photonics 9, 725-732 (2015). doi: 10.1038/nphoton.2015.182
[55] Kaikhah, K. & Loochan, F. Computer generated holograms for optical neural networks. Appl. Intell. 14, 145-160 (2001). doi: 10.1023/A:1008314025737
[56] Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004-1008 (2018). doi: 10.1126/science.aat8084
[57] Nicoletti, E. et al. Generation of λ/12 nanowires in chalcogenide glasses. Nano Lett. 11, 4218-4221 (2011). doi: 10.1021/nl202173t
[58] Yue, Z. J. et al. Nanometric holograms based on a topological insulator material. Nat. Commun. 8, 15354 (2017). doi: 10.1038/ncomms15354
[59] Blanche, P. A. et al. Holographic three-dimensional telepresence using large-area photorefractive polymer. Nature 468, 80-83 (2010). doi: 10.1038/nature09521
[60] Li, X. P. et al. Light-control-light nanoplasmonic modulator for 3D micro-optical beam shaping. Adv. Opt. Mater. 4, 70-75 (2016). doi: 10.1002/adom.201500405
[61] Hwang, C. Y. et al. Rewritable full-color computer-generated holograms based on color-selective diffractive optical components including phase-change materials. Nanoscale 10, 21648-21655 (2018). doi: 10.1039/C8NR04471F
[62] Cheng, Z. G. et al. On-chip photonic synapse. Sci. Adv. 3, e1700160 (2017). doi: 10.1126/sciadv.1700160
[63] Gu, M., Zhang, Q. M. & Lamon, S. Nanomaterials for optical data storage. Nat. Rev. Mater. 1, 16070 (2016). doi: 10.1038/natrevmats.2016.70
[64] Brunstein, M. et al. Excitability and self-pulsing in a photonic crystal nanocavity. Phys. Rev. A 85, 031803 (2012). doi: 10.1103/PhysRevA.85.031803
[65] Cho, C. H. et al. Tailoring hot-exciton emission and lifetimes in semiconducting nanowires via whispering-gallery nanocavity plasmons. Nat. Mater. 10, 669-675 (2011). doi: 10.1038/nmat3067
[66] Gill, A. A. et al. Towards the fabrication of artificial 3D microdevices for neural cell networks. Biomed. Microdevices 17, 27 (2015). doi: 10.1007/s10544-015-9929-x
[67] Feinerman, O., Rotem, A. & Moses, E. Reliable neuronal logic devices from patterned hippocampal cultures. Nat. Phys. 4, 967-973 (2008). doi: 10.1038/nphys1099
[68] Harris, J. P. et al. Advanced biomaterial strategies to transplant preformed micro-tissue engineered neural networks into the brain. J. Neural Eng. 13, 016019 (2016). doi: 10.1088/1741-2560/13/1/016019
[69] D'Avanzo, C. et al. Alzheimer's in 3D culture: challenges and perspectives. Bioessays 37, 1139-1148 (2015). doi: 10.1002/bies.201500063
[70] Mammoto, T. & Ingber, D. E. Mechanical control of tissue and organ development. Development 137, 1407-1420 (2010). doi: 10.1242/dev.024166
[71] Onoe, H. & Takeuchi, S. Microfabricated mobile microplates for handling single adherent cells. J. Micromech. Microeng. 18, 095003 (2008). doi: 10.1088/0960-1317/18/9/095003
[72] Merz, M. & Fromherz, P. Silicon chip interfaced with a geometrically defined net of snail neurons. Adv. Funct. Mater. 15, 739-744 (2005). doi: 10.1002/adfm.200400316
[73] Li, W. et al. NeuroArray: a universal interface for patterning and interrogating neural circuitry with single cell resolution. Sci. Rep. 4, 4784 (2014).
[74] Hardelauf, H. et al. High fidelity neuronal networks formed by plasma masking with a bilayer membrane: analysis of neurodegenerative and neuroprotective processes. Lab Chip 11, 2763-2771 (2011). doi: 10.1039/c1lc20257j
[75] Thalhammer, A. et al. The use of nanodiamond monolayer coatings to promote the formation of functional neuronal networks. Biomaterials 31, 2097-2104 (2010). doi: 10.1016/j.biomaterials.2009.11.109
[76] Környei, Z. et al. Cell sorting in a Petri dish controlled by computer vision. Sci. Rep. 3, 1088 (2013). doi: 10.1038/srep01088
[77] Pirlo, R. K. et al. Cell deposition system based on laser guidance. Biotechnol. J. 1, 1007-1013 (2006). doi: 10.1002/biot.200600127
[78] Dörig, P. et al. Force-controlled spatial manipulation of viable mammalian cells and micro-organisms by means of FluidFM technology. Appl. Phys. Lett. 97, 023701 (2010). doi: 10.1063/1.3462979
[79] Lozano, R. et al. 3D printing of layered brain-like structures using peptide modified gellan gum substrates. Biomaterials 67, 264-273 (2015). doi: 10.1016/j.biomaterials.2015.07.022
[80] Tang-Schomer, M. D. et al. Bioengineered functional brain-like cortical tissue. Proc. Natl. Acad. Sci. USA 111, 13811-13816 (2014). doi: 10.1073/pnas.1324214111
[81] Hinton, T. J. et al. Three-dimensional printing of complex biological structures by freeform reversible embedding of suspended hydrogels. Sci. Adv. 1, e1500758 (2015). doi: 10.1126/sciadv.1500758
[82] Gan, Z. S., Turner, M. D. & Gu, M. Biomimetic gyroid nanostructures exceeding their natural origins. Sci. Adv. 2, e1600084 (2016). doi: 10.1126/sciadv.1600084
[83] Amato, L. et al. Integrated three-dimensional filter separates nanoscale from microscale elements in a microfluidic chip. Lab Chip 12, 1135-1142 (2012). doi: 10.1039/c2lc21116e
[84] Schizas, C. et al. On the design and fabrication by two-photon polymerization of a readily assembled micro-valve. Int. J. Adv. Manuf. Technol. 48, 435-441 (2010). doi: 10.1007/s00170-009-2320-4
[85] Galanopoulos, S. et al. Design, fabrication and computational characterization of a 3D micro-valve built by multi-photon polymerization. Micromachines 5, 505-514 (2014).
[86] Raimondi, M. T. et al. Two-photon laser polymerization: from fundamentals to biomedical application in tissue engineering and regenerative medicine. J. Appl. Biomater. Funct. Mater. 10, 56-66 (2012).
[87] Torgersen, J. et al. Hydrogels for two-photon polymerization: a toolbox for mimicking the extracellular matrix. Adv. Funct. Mater. 23, 4542-4554 (2013). doi: 10.1002/adfm.201203880
[88] Yu, H. Y., Zhang, Q. M. & Gu, M. Three-dimensional direct laser writing of biomimetic neuron structures. Opt. Express 26, 32111-32117 (2018). doi: 10.1364/OE.26.032111
[89] Yu, H. Y. et al. Three-dimensional direct laser writing of neuron-inspired structures. Proceedings of the Frontiers in Optics 2017. FTu5D.2. Washington, DC, United States: Optical Society of America, 2017.
[90] Yu, H. Y., Zhang, Q. M. & Gu, M. Three-dimensional direct laser writing of ultra-low density neuron-inspired steiner tree structures. Proceedings of the Frontiers in Optics 2018. FM3D.2. Washington, DC, United States: Optical Society of America, 2018.
[91] Ding, H. B. et al. Two-photon polymerization of biocompatible hydrogels. Proceedings of the Frontiers in Optics 2017. FTu5B.3. Washington, DC, United States: Optical Society of America, 2017.
[92] Ovsianikov, A. et al. Engineering 3D cell-culture matrices: multiphoton processing technologies for biological and tissue engineering applications. Expert Rev. Med. Devices 9, 613-633 (2012). doi: 10.1586/erd.12.48
[93] Kaehr, B. et al. Guiding neuronal development with in situ microfabrication. Proc. Natl. Acad. Sci. USA 101, 16104-16108 (2004). doi: 10.1073/pnas.0407204101
[94] Kaehr, B. et al. Direct-write fabrication of functional protein matrixes using a low-cost Q-switched laser. Anal. Chem. 78, 3198-3202 (2006). doi: 10.1021/ac052267s
[95] Seidlits, S. K., Schmidt, C. E. & Shear, J. B. High-resolution patterning of hydrogels in three dimensions using direct-write photofabrication for cell guidance. Adv. Funct. Mater. 19, 3543-3551 (2009). doi: 10.1002/adfm.200901115
[96] Melissinaki, V. et al. Direct laser writing of 3D scaffolds for neural tissue engineering applications. Biofabrication 3, 045005 (2011). doi: 10.1088/1758-5082/3/4/045005
[97] Barry, J. F. et al. Optical magnetic detection of single-neuron action potentials using quantum defects in diamond. Proc. Natl. Acad. Sci. USA 113, 14133-14138 (2016). doi: 10.1073/pnas.1601513113
[98] Gruber, A. et al. Scanning confocal optical microscopy and magnetic resonance on single defect centers. Science 276, 2012-2014 (1997). doi: 10.1126/science.276.5321.2012
[99] Le Sage, D. et al. Optical magnetic imaging of living cells. Nature 496, 486-489 (2013). doi: 10.1038/nature12072
[100] Doherty, M. W. et al. Theory of the ground-state spin of the NV- center in diamond. Phys. Rev. B 85, 205203 (2012). doi: 10.1103/PhysRevB.85.205203
[101] Neumann, P. et al. Excited-state spectroscopy of single NV defects in diamond using optically detected magnetic resonance. New J. Phys. 11, 013017 (2009). doi: 10.1088/1367-2630/11/1/013017
[102] Zeeman, P. On the influence of magnetism on the nature of the light emitted by a substance. Astrophys. J. 5, 332 (1897). doi: 10.1086/140355
[103] Zeeman, P. VⅡ. Doublets and triplets in the spectrum produced by external magnetic forces. Lond. Edinb. Dublin Philos. Mag. J. Sci. 44, 55-60 (1897). doi: 10.1080/14786449708621028
[104] Rondin, L. et al. Magnetometry with nitrogen-vacancy defects in diamond. Rep. Prog. Phys. 77, 056503 (2014). doi: 10.1088/0034-4885/77/5/056503
[105] Hall, L. T. et al. High spatial and temporal resolution wide-field imaging of neuron activity using quantum NV-diamond. Sci. Rep. 2, 401 (2012). doi: 10.1038/srep00401
[106] Hsiao, W. W. W. et al. Fluorescent nanodiamond: a versatile tool for long-term cell tracking, super-resolution imaging, and nanoscale temperature sensing. Acc. Chem. Res. 49, 400-407 (2016). doi: 10.1021/acs.accounts.5b00484
[107] Hsu, T. C. et al. Labeling of neuronal differentiation and neuron cells with biocompatible fluorescent nanodiamonds. Sci. Rep. 4, 5004 (2014).
[108] Mochalin, V. N. et al. The properties and applications of nanodiamonds. Nat. Nanotechnol. 7, 11-23 (2012). doi: 10.1038/nnano.2011.209
[109] Bradac, C. et al. Effect of the nanodiamond host on a nitrogen-vacancy color-centre emission state. Small 9, 132-139 (2013). doi: 10.1002/smll.201200574
[110] Bradac, C. et al. Observation and control of blinking nitrogen-vacancy centres in discrete nanodiamonds. Nat. Nanotechnol. 5, 345-349 (2010). doi: 10.1038/nnano.2010.56
[111] Gu, M. et al. Super-resolving single nitrogen vacancy centers within single nanodiamonds using a localization microscope. Opt. Express 21, 17639-17646 (2013). doi: 10.1364/OE.21.017639