Imitating the brain with neurocomputer a “new” way towards artificial general intelligence

  • Tie-Jun HuangEmail author
Open Access


To achieve the artificial general intelligence (AGI), imitate the intelligence? or imitate the brain? This is the question! Most artificial intelligence (AI) approaches set the understanding of the intelligence principle as their premise. This may be correct to implement specific intelligence such as computing, symbolic logic, or what the AlphaGo could do. However, this is not correct for AGI, because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings. It is not wise to set such a question as the premise of the AGI mission. To achieve AGI, a practical approach is to build the so-called neurocomputer, which could be trained to produce autonomous intelligence and AGI. A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons, synapses and other essential neural components. The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body. The philosophy under the “new” approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded. This paper compares the neurocomputer with the conventional computer. The major progress about neurocomputer is also reviewed.


Artificial general intelligence (AGI) neuromorphic computing neurocomputer brain-like intelligence imitationalism 



Part of the content had been published in [86] (in Chinese), co-authored by the author of this paper.


  1. [1]
    J. McCarthy, M. L. Minsky, N. Rochester, C. E. Shannon. A proposal for the Dartmouth summer research project on artificial intelligence, AI Magazine, vol. 27, no. 4, Article number 1904, 2006.Google Scholar
  2. [2]
    J. Hawkins, S. Blakeslee. On Intelligence, New York, USA: Times Books, pp. 272, 2004.Google Scholar
  3. [3]
    T. J. Huang. Brain-like machinery-now and future. Guangming Daily, December 6, 2015. (in Chinese)Google Scholar
  4. [4]
    T. J. Huang. Could our human being create a super brain. China Reading Weekly, January 7, 2015. (in Chinese)Google Scholar
  5. [5]
    T. J. Huang. Brain-like computing. Computing Now, vol.9, no. 5, 2016.Google Scholar
  6. [6]
    H. Markram, K. Meier. The Human Brain Project–A Report to the European Commission, The HBP-PS Consortium, 2012.Google Scholar
  7. [7]
    National Academy of Engineering. Reverse-engineer the Brain. The 14 Grand Challenges for Engineering in the 21st Century, [Online], Available: http://www. engineeringchallenges. org/cms/ 8996/9109.aspx, 2008.Google Scholar
  8. [8]
    A. L. Hodgkin, A. F. Huxley. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, vol. 117, no. 4, pp. 500–544, 1952.CrossRefGoogle Scholar
  9. [9]
    M. Tsodyks, K. Pawelzik, H. Markram. Neural networks with dynamic synapses. Neural Computation, vol. 10, no. 4, pp. 821–835, 1998.CrossRefGoogle Scholar
  10. [10]
    H. Markram. The blue brain project. Nature Reviews Neuroscience, vol. 7, no. 2, pp. 153–160, 2006.MathSciNetCrossRefGoogle Scholar
  11. [11]
    The European Union’s Human Brain Project, [Online], Available:, July 25, 2016.Google Scholar
  12. [12]
    The United States’ Brain Research Through Advancing Innovative Neurotechnologies, [Online], Available:, July 25, 2016.Google Scholar
  13. [13]
    The BRAIN Initiative at NIH, [Online], Available:, July 25, 2016.Google Scholar
  14. [14]
    P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, B. Brezzo, I. Vo, S. K. Esser, R. Appuswamy, B. Taba, A. Amir, M. D. Flickner, W. P. Risk, R. Manohar, D. S. Modha. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, vol. 345, no. 6197, pp. 668–673, 2014.CrossRefGoogle Scholar
  15. [15]
    K. Meier. A mixed-signal universal neuromorphic computing system. In Proceedings of IEEE International Electron Devices Meeting, IEEE, Washington, USA, pp. 4.6.1–4.6.4, 2015.Google Scholar
  16. [16]
    J. Schemmel, J. Fieres, K. Meier. Wafer-scale integration of analog neural networks. In Proceedings of IEEE International Joint Conference on Neural Networks, IEEE, Hong Kong, China, pp. 431–438, 2008.Google Scholar
  17. [17]
    J. Von Neumann. The Computer and the Brain, Yale, USA: Yale University Press, 1958.zbMATHGoogle Scholar
  18. [18]
    A. M. Turing. Computing Machinery and Intelligence. Mind LIX, no. 236, pp. 433–460, 1950.MathSciNetCrossRefGoogle Scholar
  19. [19]
    Z. Gu, G. Pan. Neuromorphic computing. Communications of the CCF, vol. 11, no. 10, pp. 10–20, 2015. (in Chinese)Google Scholar
  20. [20]
    K. Grace. Brain Performance in TEPS, [Online], Available: Scholar
  21. [21]
    Nick. Dartmouth conference: The birth of artificial intelligence. Communications of the CCF, vol. 12, no. 3, pp. 38–43, 2016. (in Chinese)Google Scholar
  22. [22]
    N. J. Nilsson. The Quest for Artificial Intelligence: A History of Ideas and Achievements, Cambridge, UK: Cambridge University Press, 2010.Google Scholar
  23. [23]
    G. E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, vol. 313, no. 5786, pp. 504–507, 2006.MathSciNetCrossRefzbMATHGoogle Scholar
  24. [24]
    G. M. Edelman. The Mindful Brain: Cortical Organization and the Group-selective Theory of Higher Brain Function, Cambridge, USA: MIT Press, 1978.Google Scholar
  25. [25]
    G. M. Edelman. Neural Darwinism: The Theory of Neuronal Group Selection, New York, USA: Basic Books, 1987.Google Scholar
  26. [26]
    G. M. Edelman. The Remembered Present: A Biological Theory of Consciousness, New York, USA: Basic Books, 1989.Google Scholar
  27. [27]
    G. N. Reeke, O. Sporns, G. M. Edelman. Synthetic neural modeling: The Darwin series of recognition automata. Proceedings of the IEEE, vol. 78, no. 9, pp. 1498–1530, 1990.CrossRefGoogle Scholar
  28. [28]
    E. M. Izhikevich, G. M. Edelman. Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 9, pp. 3593–3598, 2008.CrossRefGoogle Scholar
  29. [29]
    J. L. Krichmar, G. M. Edelman. Brain-based devices: Intelligent systems based on principles of the nervous system. In Proceedings of the IEEE/RSJ International. Conference on Intelligent Robots and Systems, IEEE, Las Vegas, USA, vol. 1, pp. 940–945, 2003.Google Scholar
  30. [30]
    G. M. Edelman. Learning in and from brain-based device. Science, vol. 318, no. 5853, pp. 1103–1105, 2007.CrossRefGoogle Scholar
  31. [31]
    Brain-based Devices, [Online], Available: http://www.nsi. edu/ nomad/.Google Scholar
  32. [32]
    G. Indiveri, T. K. Horiuchi. Frontiers in neuromorphic engineering. Frontiers in Neuroscience, vol. 5, Article number 118, 2011.Google Scholar
  33. [33]
    C. Mead. Analog VLSI and Neural Systems, Reading, USA: Addison-Wesley Publishers, 1989.zbMATHGoogle Scholar
  34. [34]
    C. Mead. Neuromorphic electronic systems. Proceedings of the IEEE, vol. 78, no. 10, pp. 1629–1636, 1990.CrossRefGoogle Scholar
  35. [35]
    S. K. Cohen. Interview with Carver A. Mead (1934–), California Institute of Technology, California, USA, July 17, 1996.Google Scholar
  36. [36]
    C. Mead, M. Ismail. Analog VLSI Implementation of Neural Systems, Boston, USA: Kluwer Academic Publishers, 1989.CrossRefGoogle Scholar
  37. [37]
    M. Mahowald. An Analog VLSI System for Stereoscopic Vision, Boston, USA: Kluwer Academic Publishers, 1994.CrossRefGoogle Scholar
  38. [38]
    K. A. Boahen. A retinomorphic vision system. IEEE Micro, vol. 16, no. 5, pp. 30–39, 1996.CrossRefGoogle Scholar
  39. [39]
    B. V. Benjamin, P. R. Gao, E. McQuinn, S. Choudhary, A. R. Chandrasekaran, J. M. Bussat, R. Alvarez-Icaza, J. V. Arthur, P. A. Merolla, K. Boahen. Neurogrid: A mixedanalog-digital multichip system for large-scale neural simulations. Proceedings of the IEEE, vol. 102, no. 5, pp. 699–716, 2014.CrossRefGoogle Scholar
  40. [40]
    A. S. Cassidy, P. Merolla, J. V. Arthur, S. K. Esser, B. Jackson, R. Alvarez-Icaza, P. Datta, J. Sawada, T. M. Wong, V. Feldman, A. Amir, D. Ben-Dayan Rubin, F. Akopyan, E. McQuinn, W. P. Risk, D. S. Modha. Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores. In Proceedings of International Joint Conference on Neural Networks, IEEE, Dallas, USA, pp. 1–10, 2013.Google Scholar
  41. [41]
    P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, B. Brezzo, I. Vo, S. K. Esser, R. Appuswamy, B. Taba, A. Amir, M. D. Flickner, W. P. Risk, R. Manohar, D. S. Modha. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, vol. 345, no. 6197, pp. 668–673, 2014.CrossRefGoogle Scholar
  42. [42]
    J. Schemmel, D. Briiderle, A. Griibl, M. Hock, K. Meier, S. Millner. A wafer-scale neuromorphic hardware system for large-scale neural modeling. In Proceedings of IEEE International Symposium on Circuits and Systems, IEEE, Paris, France, pp. 1947–1950, 2010.Google Scholar
  43. [43]
    S. Scholze, H. Eisenreich, S. Höppner, G. Ellguth, S. Henker, M. Ander, S. Hänzsche, J. Partzsch, C. Mayr, R. Schüffny. A 32 GBit/S communication SoC for a waferscale neuromorphic system. Integration, the VLSI Journal, vol. 45, no. 1, pp. 61–75, 2012.CrossRefGoogle Scholar
  44. [44]
    D. Brüderle, E. Müller, A. Davison, E. Muller, J. Schemmel, K. Meier. Establishing a novel modeling tool: A pythonbased interface for a neuromorphic hardware system. Frontiers in Neuroinformatics, vol. 3, Article number 17, 2009.Google Scholar
  45. [45]
    S. B. Furber, F. Galluppi, S. Temple, L. A. Plana. The SpiNNaker project. Proceedings of the IEEE, vol. 102, no. 5, pp. 652–665, 2014.CrossRefGoogle Scholar
  46. [46]
    A. D. Brown, S. B. Furber, J. S. Reeve, J. D. Garside, K. J. Dugan, L. A. Plana, S. Temple. SpiNNaker-programming model. IEEE Transactions on Computers, vol. 64, no. 6, pp. 1769–1782, 2015.MathSciNetGoogle Scholar
  47. [47]
    S. Furber. Large-scale neuromorphic computing systems. Journal of Neural Engineering, vol. 3, no. 5, Article number 051001, 2016.Google Scholar
  48. [48]
    T. Tuma, A. Pantazi, M. L. Gallo, A. Sebastian, E. Eleftheriou. Stochastic phase-change neurons. Nature Nanotechnology, vol. 11, no. 8, pp. 693–699, 2016.CrossRefGoogle Scholar
  49. [49]
    D. B. Strukov, G. S. Snider, D. R. Stewart, R. S. Williams. The missing memristor found. Nature, vol. 453, no. 7191, pp. 80–83, 2008.CrossRefGoogle Scholar
  50. [50]
    J. J. Yang, M. D. Pickett, X. M. Li, D. A. Ohlberg, D. R. Stewart, R. S. Williams. Memristive switching mechanism for metal/oxide/metal nanodevices. Nature Nanotechnology, vol. 3, no. 7, pp. 429–433, 2008.CrossRefGoogle Scholar
  51. [51]
    G. S. Snider. Spike-timing-dependent learning in memristive nanodevices. In Proceedings of IEEE International Symposium on Nanoscale Architectures, IEEE, Anaheim, USA, pp. 85–92, 2008.Google Scholar
  52. [52]
    S. H. Jo, T. Chang, I. Ebong, B. B. Bhadviya, P. Mazumder, W. Lu. Nanoscale memristor device as synapse in neuromorphic systems. Nano Letters, vol. 10, no. 4, pp. 1297–1301, 2010.CrossRefGoogle Scholar
  53. [53]
    T. Ohno, T. Hasegawa, T. Tsuruoka, K. Terabe, J. K. Gimzewski, M. Aono. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nature Materials, vol. 10, no. 8, pp. 591–595, 2011.CrossRefGoogle Scholar
  54. [54]
    R. Berdan, E. Vasilaki, A. Khiat, G. Indiveri, A. Serb, T. Prodromakis. Emulating short-term synaptic dynamics with memristive devices. Scientific Reports, vol.6, Article number 18639, 2016.Google Scholar
  55. [55]
    D. Kuzum, R. G. D. Jeyasingh, B. Lee, H. S. P. Wong. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Letters, vol. 12, no. 5, pp. 2179–2186, 2012.CrossRefGoogle Scholar
  56. [56]
    D. Kuzum, S. M. Yu, H. P. Wong. Synaptic electronics: Materials, devices and applications. Nanotechnology, vol. 24, no. 38, Article number 382001, 2013.Google Scholar
  57. [57]
    M. D. Pickett, G. Medeiros-Ribeiro, R. S. Williams. A scalable neuristor built with Mott memristors. Nature Materials, vol. 12, no. 2, pp. 114–117, 2013.CrossRefGoogle Scholar
  58. [58]
    A. F. Vincent, J. Larroque, N. Locatelli, N. B. Romdhane, O. Bichler, C. Gamrat, W. S. Zhao, J. O. Klein, S. Galdin-Retailleau, D. Querlioz. Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems. IEEE Transactions on Biomedical Circuits and Systems, vol. 9, no. 2, pp. 166–174, 2015.CrossRefGoogle Scholar
  59. [59]
    Y. F. Chang, B. Fowler, Y. C. Chen, F. Zhou, C. H. Pan, T. C. Chang, J. C. Lee. Demonstration of synaptic behaviors and resistive switching characterizations by proton exchange reactions in silicon oxide. Scientific Reports, vol.6, Article number 21268. 2016.Google Scholar
  60. [60]
    Y. C. Yang, B. Chen, W. D. Lu. Memristive physically evolving networks enabling the emulation of heterosynaptic plasticity. Advanced Materials, vol. 27, no. 47, pp. 7720–7727, 2015.CrossRefGoogle Scholar
  61. [61]
    Z.W.Wang, M. H. Yin, T. Zhang, Y. M. Cai, Y. Y. Wang, Y. C. Yang, R. Huang. Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing. Nanoscale, vol. 8, no. 29, pp. 14015–14022, 2016.CrossRefGoogle Scholar
  62. [62]
    Y. C. Yang, J. Lee, S. Lee, C. H. Liu, Z. H. Zhong, W. Lu. Oxide resistive memory with functionalized graphene as built-in selector element. Advanced Materials, vol. 26, no. 22, pp. 3693–3699, 2014.CrossRefGoogle Scholar
  63. [63]
    J. F. Kang, B. Gao, P. Huang, H. T. Li, Y. D. Zhao, Z. Chen, C. Liu, L. F. Liu, X. Y. Liu. Oxide-based RRAM: Requirements and challenges of modeling and simulation. In Proceedings of IEEE International Electron Devices Meeting, IEEE, Washington, USA, pp. 5.4.1–5.4.4. 2015.Google Scholar
  64. [64]
    Y. Bai, H. Q. Wu, R. Wu, Y. Zhang, N. Deng, Z. P. Yu, H. Qian. Study of multi-level characteristics for 3D vertical resistive switching memory. Scientific Reports, vol.4, Article number 5780, 2014.Google Scholar
  65. [65]
    L. Deng, D. Wang, Z. Y. Zhang, P Tang, G. Q. Li, J. Pei. Energy consumption analysis for various memristive networks under different learning strategies. Physics Letters A, vol. 380, no. 7–8, pp. 903–909, 2016.CrossRefGoogle Scholar
  66. [66]
    C. H. Wan, X. Z, Zhang, X. L. Gao, J. M. Wang, X. Y. Tan. Geometrical enhancement of low-field magnetoresistance in silicon. Nature, vol. 477, no. 7364, pp. 304–307, 2011.CrossRefGoogle Scholar
  67. [67]
    H. Tian, W. T. Mi, X. F. Wang, H. M. Zhao, Q. Y. Xie, C. Li, Y. X. Li, Y. Yang, T. L. Ren. Graphene dynamic synapse with modulatable plasticity. Nano Letters, vol. 15, no. 12, pp. 8013–8019, 2015.CrossRefGoogle Scholar
  68. [68]
    L. Q. Zhu, C. J. Wan, L. Q. Guo, Y. Shi, Q. Wan. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nature Communications, vol. 5, Article number 3158, 2014.Google Scholar
  69. [69]
    C. J. Wan, L. Q. Zhu, Y. H. Liu, P. Feng, Z. P. Liu, H. L. Cao, P. Xiao, Y. Shi, Q. Wan. Proton-conducting graphene oxide-coupled neuron transistors for brain-inspired cognitive systems. Advanced Materials, vol. 28, no. 18, pp. 3557–3563, 2016.CrossRefGoogle Scholar
  70. [70]
    N. Liu, L. Q. Zhu, P. Feng, C. J. Wan, Y. H. Liu, Y. Shi, Q. Wan. Flexible sensory platform based on oxide-based neuromorphic transistors. Scientific Reports, vol. 5, Article number 18082, 2015.Google Scholar
  71. [71]
    J. M. Zhou, N. Liu, L. Q. Zhu, Y. Shi, Q. Wan. Energyefficient artificial synapses based on flexible IGZO electricdouble- layer transistors. IEEE Electron Device Letters, vol. 36, no. 2, pp. 198–200, 2015.CrossRefGoogle Scholar
  72. [72]
    M. J. Xia, K. Y. Ding, F. Rao, X. B. Li, L. C. Wu, Z. T. Song. Aluminum-centered tetrahedron-octahedron transition in advancing Al-Sb-Te phase change properties. Scientific Reports, vol. 5, Article number 8548, 2015.Google Scholar
  73. [73]
    F. Rao, Z. T. Song, Y. Cheng, X. S. Liu, M. J. Xia, W. Li, K. Y. Ding, X. F. Feng, M. Zhu, S. L. Feng. Direct observation of titanium-centered octahedra in titanium-antimonytellurium phase-change material. Nature Communications, vol. 6, Article number 10040, 2015.Google Scholar
  74. [74]
    Y. Li, Y. P. Zhong, J. J. Zhang, L. Xu, Q. Wang, H. J. Sun, H. Tong, X. M. Cheng, X. S. Miao. Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems. Scientific Reports, vol.4, Article number 4906, 2014.Google Scholar
  75. [75]
    Z. G. Zeng, W. X. Zheng. Multistability of neural networks with time-varying delays and concave-convex characteristics. IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 2, pp. 293–305, 2012.CrossRefGoogle Scholar
  76. [76]
    G. Bao, Z. G. Zeng. Analysis and design of associative memories based on recurrent neural network with discontinuous activation functions. Neurocomputing, vol. 77, no. 1, pp. 101–107, 2012.CrossRefGoogle Scholar
  77. [77]
    S. P. Wen, T. W. Huang, Z. G. Zeng, Y. Chen, P. Li. Circuit design and exponential stabilization of memristive neural networks. Neural Networks, vol. 63, pp. 48–56, 2015.CrossRefzbMATHGoogle Scholar
  78. [78]
    Y. P. Zhong, Y. Li, L. Xu, X. S. Miao. Simple square pulses for implementing spike-timing-dependent plasticity in phase-change memory. Physica Status Solidi (RRL)–Rapid Research Letters, vol. 9, no. 7, pp. 414–419, 2015.CrossRefGoogle Scholar
  79. [79]
    Z. S. Tang, L. Fang, N. Xu, R. L. Liu. Forming compliance dominated memristive switching through interfacial reaction in Ti/TiO2/Au structure. Journal of Applied Physics, vol. 118, no. 18, Article number 185309, 2015.Google Scholar
  80. [80]
    X. D. Fang, X. J. Yang, J. J. Wu, X. Yi. A compact SPICE model of unipolar memristive devices. IEEE Transactions on Nanotechnology, vol. 12, no. 5, pp. 843–850, 2013.CrossRefGoogle Scholar
  81. [81]
    Global Brain Workshop 2016 Attendees. Grand Challenges for Global Brain Sciences, [Online], Available:, April, 2016.Google Scholar
  82. [82]
    R. Mizutani, R. Saiga, A. Takeuchi, K. Uesugi, Y. Suzuki. Three-dimensional Network of Drosophila Brain Hemisphere, [Online], Available: arxiv/papers/1609/1609.02261.pdf, September 18, 2016.Google Scholar
  83. [83]
    T. D. Albright, T. M. Jessell, E. R. Kandel, M. I. Posner. Progress in the neural sciences in the century after Cajal (and the mysteries that remain). Annals of the New York Academy of Sciences, vol. 929, pp. 11–40, 2001.CrossRefGoogle Scholar
  84. [84]
    X. Y. Zhang, C. L. Zhou. From biological consciousness to machine consciousness: An approach to make smarter machines. International Journal of Automation and Computing, vol. 10, no. 6, pp. 498–505, 2013.CrossRefGoogle Scholar
  85. [85]
    The Economist. Neuromorphic Computing: The Machine of a New Soul, August 5, 2013.Google Scholar
  86. [86]
    T. J. Huang, L. P. Shi, H. J. Tang, G. Pan, Y. J. Chen, J. Q. Yu. Brain-like computing advances and trends. In Proceedings of Computer Science and Technology Developing Report of the China Computer Federation, China Machine Press, Beijing, China, 2011. (in Chinese)Google Scholar

Copyright information

© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.School of Electronic Engineering and Computer SciencePeking UniversityBeijingChina

Personalised recommendations