[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 |