[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Neuromorphic electronics based on copying and pasting the brain

Nature Electronics volume 4, pages 635–644 (2021)Cite this article

Subjects

Abstract

Reverse engineering the brain by mimicking the structure and function of neuronal networks on a silicon integrated circuit was the original goal of neuromorphic engineering, but remains a distant prospect. The focus of neuromorphic engineering has thus been relaxed from rigorous brain mimicry to designs inspired by qualitative features of the brain, including event-driven signalling and in-memory information processing. Here we examine current approaches to neuromorphic engineering and provide a vision that returns neuromorphic electronics to its original goal of reverse engineering the brain. The essence of this vision is to ‘copy’ the functional synaptic connectivity map of a mammalian neuronal network using advanced neuroscience tools and then ‘paste’ this map onto a high-density three-dimensional network of solid-state memories. Our copy-and-paste approach could potentially lead to silicon integrated circuits that better approximate computing traits of the brain, including low power, facile learning, adaptation, and even autonomy and cognition.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Contemporary neuromorphic research.
Fig. 2: Copying the NNN.
Fig. 3: Pasting.
Fig. 4: Neuromorphic scaling using 3D integration and packaging technology.

TechInsights (Ray Fontaine, http://rfontaine@techinsights.com; e (right))

Similar content being viewed by others

References

  1. Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990).

    Article  Google Scholar 

  2. Mead, C. Analog VLSI and Neural Systems (Addison-Wesley, 1989).

  3. Liu, S.-C. & Delbruck, T. Neuromorphic sensory systems. Curr. Opin. Neurobiol. 20, 288–295 (2010).

    Article  Google Scholar 

  4. Lichtsteiner, P., Posch, C. & Delbruck, T. A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor. IEEE J. Solid State Circuits 43, 566–576 (2008).

    Article  Google Scholar 

  5. Son, B. et al. A 640 × 480 dynamic vision sensor with a 9 μm pixel and 300 Meps address-event representation. In 2017 IEEE International Solid-State Circuits Conference (ISSCC) 66–67 (IEEE, 2017).

  6. Merolla, P. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).

    Article  Google Scholar 

  7. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).

    Article  Google Scholar 

  8. Sheridan, P. M. et al. Sparse coding with memristor networks. Nat. Nanotechnol. 12, 784–790 (2017).

    Article  Google Scholar 

  9. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Article  Google Scholar 

  10. Alibart, F., Zamanidoost, E. & Strukov, D. B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4, 2072 (2013).

    Article  Google Scholar 

  11. Ambrogio, S. et al. Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–67 (2018).

    Article  Google Scholar 

  12. Choi, S., Shin, J. H., Lee, J., Sheridan, P. & Lu, W. D. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett. 17, 3113–3118 (2017).

    Article  Google Scholar 

  13. Wang, Z. et al. Resistive switching materials for information processing. Nat. Rev. Mater. 5, 173–195 (2020).

    Article  Google Scholar 

  14. Lin, P. et al. Three-dimensional memristor circuits as complex neural networks. Nat. Electron. 3, 225–232 (2020).

    Article  Google Scholar 

  15. Wang, Z. et al. Reinforcement learning with analogue memristor arrays. Nat. Electron. 2, 115–124 (2019).

    Article  Google Scholar 

  16. Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).

    Article  Google Scholar 

  17. Jang, H. et al. An atomically thin optoelectronic machine vision processor. Adv. Mater. 32, 2002431 (2020).

    Article  Google Scholar 

  18. Mahowald, M. & Douglas, R. A silicon neuron. Nature 354, 515–518 (1991).

    Article  Google Scholar 

  19. Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).

    Google Scholar 

  20. Ryckebusch, S., Bower, J. M. & Mead, C. Modeling small oscillating biological networks in analog VLSI. Adv. Neural Inf. Process. Syst. 1, 384–393 (1989).

    Google Scholar 

  21. Hahnloser, R. H. R., Sarpeshkar, R., Mahowald, M., Douglas, R. & Seung, H. S. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405, 947–951 (2000).

    Article  Google Scholar 

  22. Mahowald, M. & Mead, C. The silicon retina. Sci. Am. 264, 76–82 (1991).

    Article  Google Scholar 

  23. Abbott, J. et al. A nanoelectrode array for obtaining intracellular recordings from thousands of connected neurons. Nat. Biomed. Eng. 4, 232–241 (2020).

    Article  Google Scholar 

  24. Kim, K. Silicon technologies and solutions for the data-driven world. In 2015 IEEE International Solid-State Circuits Conference (ISSCC) 8–14 (IEEE, 2015).

  25. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  Google Scholar 

  26. Song, J. et al. An 11.5TOPS/W 1024-MAC butterfly structure dual-core sparsity-aware neural processing unit in 8 nm flagship mobile SoC. In 2019 IEEE International Solid-State Circuits Conference (ISSCC) 130–131 (IEEE, 2019).

  27. Wang, Z. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137–145 (2018).

    Article  Google Scholar 

  28. Ambrogio, S. et al. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses. Front. Neurosci. 10, 56 (2016).

    Article  Google Scholar 

  29. Alivisatos, P. et al. Nanotools for neuroscience and brain activity mapping. ACS Nano 7, 1850–1866 (2013).

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Cazemier, J. L., Clasca, F. & Tiesinga, P. H. E. Connectomic analysis of brain networks: novel techniques and future directions. Front. Neuroanat. 10, 110 (2016).

    Article  Google Scholar 

  32. White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. B 314, 1–340 (1986).

    Google Scholar 

  33. Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 (2020).

    Article  Google Scholar 

  34. Sasaki, T., Minamisawa, G., Takahashi, N., Matsuki, N. & Ikegaya, Y. Reverse optical trawling for synaptic connections in situ. J. Neurophysiol. 102, 636–643 (2009).

    Article  Google Scholar 

  35. Petreanu, L., Huber, D., Sobczyk, A. & Svoboda, K. Channelrhodopsin-2-assisted circuit mapping of long-range callosal projections. Nat. Neurosci. 10, 663–668 (2007).

    Article  Google Scholar 

  36. Shemesh, O. A. et al. Temporally precise single-cell-resolution optogenetics. Nat. Neurosci. 20, 1796–1806 (2017).

    Article  Google Scholar 

  37. Jäckel, D. et al. Combination of high-density microelectrode array and patch clamp recordings to enable studies of multisynaptic integration. Sci. Rep. 7, 978 (2017).

    Article  Google Scholar 

  38. Perin, R., Berger, T. K. & Markram, H. A synaptic organizing principle for cortical neuronal groups. Proc. Natl Acad. Sci. USA 108, 5419–5424 (2011).

    Article  Google Scholar 

  39. Frey, U. et al. Switch-matrix-based high-density microelectrode array in CMOS technology. IEEE J. Solid State Circuits 45, 467–482 (2010).

    Article  Google Scholar 

  40. Tsai, D., Sawyer, D., Bradd, A., Yuste, R. & Shepard, K. L. A very large-scale microelectrode array for cellular-resolution electrophysiology. Nat. Commun. 8, 1802 (2017).

    Article  Google Scholar 

  41. Abbott, J. et al. The design of a CMOS nanoelectrode array with 4096 current-clamp/voltage-clamp amplifiers for intracellular recording/stimulation of mammalian neurons. IEEE J. Solid State Circuits 55, 2567–2582 (2020).

    Article  Google Scholar 

  42. Abbott, J. et al. CMOS nanoelectrode array for all-electrical intracellular electrophysiological imaging. Nat. Nanotechnol. 12, 460–466 (2017).

    Article  Google Scholar 

  43. Robinson, J. T. et al. Vertical nanowire electrode arrays as a scalable platform for intracellular interfacing to neuronal circuits. Nat. Nanotechnol. 7, 180–184 (2012).

    Article  Google Scholar 

  44. Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).

    Article  Google Scholar 

  45. Sanes, J. R. & Zipursky, S. L. Design principles of insect and vertebrate visual systems. Neuron 66, 15–36 (2010).

    Article  Google Scholar 

  46. Wilson, D. A. & Sullivan, R. M. Cortical processing of odor objects. Neuron 72, 506–519 (2011).

    Article  Google Scholar 

  47. Lee, Y. K. et al. Embedded STT-MRAM in 28-nm FDSOI logic process for industrial MCU/IoT application. In 2018 IEEE Symposium on VLSI Technology 181–182 (IEEE, 2018).

  48. Chung, H. et al. A 58 nm 1.8 V 1 Gb PRAM with 6.4 MB/s program BW. In 2011 IEEE International Solid-State Circuits Conference (ISSCC) 500–501 (IEEE, 2011).

  49. Fackenthal, R. et al. A 16 Gb ReRAM with 200 MB/s write and 1 GB/s read in 27 nm technology. In 2014 IEEE International Solid-State Circuits Conference (ISSCC) 338–339 (IEEE, 2014).

  50. Lee, M.-J. et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5–x/TaO2–x bilayer structures. Nat. Mater. 10, 625–630 (2011).

    Article  Google Scholar 

  51. Xu, R. et al. Vertical MoS2 double layer memristor with electrochemical metallization as an atomic-scale synapse with switching thresholds approaching 100 mV. Nano Lett. 19, 2411–2417 (2019).

    Article  Google Scholar 

  52. Jo, S. H. et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297–1301 (2010).

    Article  Google Scholar 

  53. Park, K.-T. et al. Three-dimensional 128 Gb MLC vertical NAND flash-memory with 24-WL stacked layers and 50 MB/s high-speed programming. In 2014 IEEE International Solid-State Circuits Conference (ISSCC) 334–335 (IEEE, 2014).

  54. Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015).

    Article  Google Scholar 

  55. Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015).

    Article  Google Scholar 

  56. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).

    Article  Google Scholar 

  57. Kuzum, D., Jeyasingh, R. G. D., Lee, B. & Wong, H.-S. P. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12, 2179–2186 (2012).

    Article  Google Scholar 

  58. Marx, V. Neuroscience waves to the crowd. Nat. Methods 10, 1069–1074 (2013).

    Article  Google Scholar 

  59. Kim, D.-W. & Hwang, T. The future of advanced package solutions. In 2019 IEEE Symposium on VLSI Technology 48–49 (IEEE, 2019).

  60. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A. & Hudspeth, A. J. Principles of Neural Science 5th edn (McGraw-Hill, 2012).

  61. Bock, D. D. et al. Network anatomy and in vivo physiology of visual cortical neurons. Nature 471, 177–182 (2011).

    Article  Google Scholar 

  62. Briggman, K. L. & Bock, D. D. Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012).

    Article  Google Scholar 

  63. Briggman, K. L., Helmstaedter, M. & Denk, W. Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011).

    Article  Google Scholar 

  64. Kleinfeld, D. et al. Large-scale automated histology in the pursuit of connectomes. J. Neurosci. 31, 16125–16138 (2011).

    Article  Google Scholar 

  65. Lichtman, J. W. & Sanes, J. R. Ome sweet ome: what can the genome tell us about the connectome? Curr. Opin. Neurobiol. 18, 346–353 (2008).

    Article  Google Scholar 

  66. Morgan, J. L. & Lichtman, J. W. Why not connectomics? Nat. Methods 10, 494–500 (2013).

    Article  Google Scholar 

  67. Berdan, R. et al. Emulating short-term synaptic dynamics with memristive devices. Sci. Rep. 6, 18639 (2016).

    Article  Google Scholar 

  68. Kim, M.-K. & Lee, J.-S. Short-term plasticity and long-term potentiation in artificial biosynapses with diffusive dynamics. ACS Nano 12, 1680–1687 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. J. Kim, S. Jung, H. Lee and H. Kim of Samsung Advanced Institute of Technology, J. Abbott, T. Ye, K. Krenek, R. Gertner, S. Ban, Y. Kim, L. Qin, W. Wu, R. Xu, H. S. Jung and J. Wang of Harvard University and H. J. Baek (Executive Vice President), J. Song (Executive Vice President), K. Choi (Senior Vice President), S. Yoon (Vice President), T. Hwang, J. Lim, D. Kwon, Y. Kim and J. Kim of Samsung Electronics for discussions, advice and/or contributions to materials used here from original research articles.

Author information

Authors and Affiliations

  1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Donhee Ham

  2. Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, Republic of Korea

    Donhee Ham & Sungwoo Hwang

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

    Hongkun Park

  4. Department of Physics, Harvard University, Cambridge, MA, USA

    Hongkun Park

  5. Samsung Electronics, Hwaseong, Republic of Korea

    Sungwoo Hwang & Kinam Kim

Authors
  1. Donhee Ham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongkun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sungwoo Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kinam Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.H., H.P., S.H. and K.K. conceived this Perspective. D.H., H.P., S.H. and K.K. wrote the manuscript.

Corresponding authors

Correspondence to Donhee Ham, Hongkun Park, Sungwoo Hwang or Kinam Kim.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Electronics thanks Yoeri van de Burgt and Huaqiang Wu for their contribution to the peer review of this work.

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ham, D., Park, H., Hwang, S. et al. Neuromorphic electronics based on copying and pasting the brain. Nat Electron 4, 635–644 (2021). https://doi.org/10.1038/s41928-021-00646-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-021-00646-1

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing