[ 1 ] Lin C D, Yang Z L, Huang X T. A dictionary of psychology [M]. Shanghai: Shanghai Publishing House, 2003. Chinese.

[ 2 ] Turing A M. Computing machinery intelligence [J]. Mind, 1950, 59: 433–460.

[ 3 ] Chomsky N. Three models for the description of language [J]. IEEE Transactions on Information Theory, 1956, 2(3): 113–124. link1

[ 4 ] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504– 507. link1

[ 5 ] McCulloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity [J]. The Bulletin of Mathematical Biophysics, 1943, 5(4): 115–133. link1

[ 6 ] Hebb D O. The organization of behavior [M]. New York: Wiley, 1949.

[ 7 ] Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain [J]. Psychological Review, 1958, 65(6): 386–408. link1

[ 8 ] Minsky M, Papert S. Perceptrons [M]. Oxford: MIT Press, 1969.

[ 9 ] Werbos P J. Backpropagation through time: What it does and how to do it [J]. Proceedings of the IEEE, 1990, 78(10): 1550– 1560. link1

[10] Hopfield J J. Neural networks and physical systems with emergent collective computational abilities [J]. Proceedings of the National Academy of Sciences, 1982, 79(8): 2554–2558. link1

[11] Ackley D H, Hinton G E, Sejnowski T J. A learning algorithm for elmholtz machines [J]. Cognitive Science, 1985, 9(1): 147– 169.

[12] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors [J]. Nature, 1986, 323: 533– 536. link1

[13] Hubel D H, Wiesel T N. Early exploration of the visual cortex [J]. Neuron, 1998, 20: 401–412. link1

[14] Lecun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition [J]. Neural Computation, 1989 (1): 541–551. link1

[15] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [C]. Nevada: International Conference on Neural Information Processing Systems, 2012. link1

[16] Chen T, Du Z, Sun N, et al. DianNao: A small-footprint highthroughput accelerator for ubiquitous machine learning [C]. Salt Lake City: Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, 2014.

[17] Carver M. Analog VLSI and neural systems [M]. Boston: Addison-Wesley Mass, 1989.

[18] Wolfgang M. Networks of spiking neurons: The third generation of neural network models [J]. Neural Networks, 1997, 10(9): 1659–1671. link1

[19] Merolla P A. A million spiking-neuron integrated circuit with a scalable communication network and interface [J]. Science, 2014, 345 (6197): 668–673. link1

[20] Chua L O. Memristor—The missing circuit element [J]. IEEE Transactions on Circuit Theory, 1971, 18(5): 507–519. link1

[21] Dmitri B S, Gregory S S, Duncan R S. The missing memristor found [J]. Nature, 2008, 453: 80–83.

[22] Borghetti J. Memristive switches enable stateful logic operations via material implication [J]. Nature, 2010, 464: 873–876. link1

[23] Irem B, Manuel L G, Nandakumar S R, et al. Neuromorphic computing with multi-memristive synapses [J]. Nature Communication, 2018, 9: 2514–2529. link1

[24] Hochreiter S, Schmidhuber J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735–1780. link1

[25] Andrea B, Caswell B, Benigno U, et al. Vector-based navigation using grid-like representations in artificial agents [J]. Nature, 2018, 557: 429–433. link1

[26] Collins A M, Quillian M R. Retrieval time from semantic memory [J]. Journal of verbal learning and verbal behavior, 1969, 8(2): 240–248. link1

[27] McClelland J, Rumelhart D. An interactive activation model of context effects in letter perception: Part 1. An account of basic findings [J]. Psychology Review, 1981, 88: 375–407. link1

[28] Bengio Y, Ducharme R, Vincent P, et al. A neural probabilistic language model [J]. Journal of Machine Learning Research, 2003, (3): 1137–1155. link1

[29] Mikolov T, Chen K, Corrado C, et al. Efficient estimation of word representation in vector space [C]. Florida: Proceedings of Workshop at ICLR, 2013. link1

[30] Testolin A, Stoianov I, Zorzi M. Letter perception emerges from unsupervised deep learning and recycling of natural image features [J]. Human Behaviour, 2017, 1(9): 657–664. link1

[31] Wiener N. Cybernetics: Or control and communication in the animal and the machine [M]. Cambridge: The MIT Press, 1948.

[32] Minsky M L. Theory of neural-analog reinforcement systems and its application to the brain-model problem [D]. Princeton: Princeton University (Doctoral dissertation), 1954.

[33] Zhuang Y, Wu F, Chen C, et al. Challenges and opportunities: From big data to knowledge in AI 2.0 [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3–14. link1

[34] Li W, Wu W, Wang H, et al. Crowd intelligence in AI 2.0 era [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 15–43. link1

[35] Peng Y, Zhu W, Zhao Y, et al. Cross-media analysis and reasoning: Advances and directions [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 44–57. link1

[36] Zheng N, Liu Z, Ren P, et al. Hybrid-augmented intelligence: Collaboration and cognition [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 153–179. link1

[37] Tian Y, Chen X, Xiong H, et al. Towards human-like and transhuman perception in AI 2.0: A review [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 58–67. link1

[38] Zhang T, Li Q, Zhang C, et al. Current trends in the development of intelligent unmanned autonomous systems [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 68–85. link1

[39] Li B, Hou B, Yu W, et al. Applications of artificial intelligence in intelligent manufacturing: A review [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 86–96. link1

[40] Marblestone A H, Wayne G, Kording K P. Towards an integration of deep learning and neuroscience [J]. Frontiers in Computational Neuroscience, 2016, 10(5): 1–60. link1