9. Ambrogio S, Balatti S, Nardi F, et al. Spike-timing dependent plasticity in a transistor-selected resistive switching memory. Nanotechnology, 2013, 24(38): 384012.
[2]
10. Thomas A. Memristor-based neural networks. J Phys D Appl Phys, 2013, 46(9): 093001.
[3]
11. Ohno T, Hasegawa T, Tsuruoka T, et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater, 2011, 10(8): 591-595.
[4]
12. Chang Ting, Jo S H, Lu Wei. Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano, 2011, 5(9): 7669-7676.
[5]
13. Kim H, Sah M P, Yang Changju, et al. Neural synaptic weighting with a pulse-based memristor circuit. IEEE Transactions on Circuits and Systems I-Regular Papers, 2012, 59(1): 148-158.
[6]
20. Wu X, Saxena V, Zhu K. A CMOS spiking neuron for dense memristor-synapse connectivity for brain-inspired computing. Computer Science, 2015: 534-537.
[7]
21. Hu M, Chen Y, Yang J J, et al. A memristor-based dynamic synapse for spiking neural networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2016, PP(99): 1.
[8]
22. Al-Shedivat M, Naous R, Cauwenberghs G, et al. Memristors empower spiking neurons with stochasticity. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2015, 5(2): 242-253.
[9]
31. Wan L, Luo Y, Song S, et al. Efficient neuron architecture for FPGA-based spiking neural networks// 2016 27th Irish Signals and Systems Conference (ISSC). Londonderry: IEEE, 2016: 1-6.
[10]
27. Shin S, Kim K, Kang S M S. Memristor macromodel and its application to neuronal spike generation// 2013 European Conference on Circuit Theory and Design (ECCTD). Dresden: IEEE, 2013: 1-4.
[11]
28. Lecerf G, Tomas J, Boyn S, et al. Silicon neuron dedicated to memristive spiking neural networks//2014 IEEE International Symposium on Circuits and Systems (ISCAS). Melbourne: 2014: 1568-1571.
[12]
29. Vaidyanathan S, Volos C K, Kyprianidis I M, et al. Memristor: a new concept in synchronization of coupled neuromorphic circuits. Journal of Engineering Science & Technology Review, 2015, 8(2): 157-173.
[13]
30. Sah M P, Yang Changju, Kim H, et al. A voltage mode memristor bridge synaptic circuit with memristor emulators. Sensors, 2012, 12(3): 3587-3604.
39. Alibart F, Zamanidoost E, Strukov D B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat Commun, 2013, 4(3): 131-140.
32. Wang Runchun, Hamilton T J, Tapson J, et al. An FPGA design framework for large-scale spiking neural networks//2014 IEEE International Symposium on Circuits and Systems (ISCAS). Melbourne: 2014: 457-460.
[18]
33. Borghetti J, Snider G S, Kuekes P J, et al. 'Memristive' switches enable 'stateful' logic operations via material implication. Nature, 2010, 464(7290): 873-876.
[19]
34. Gaba S, Sheridan P, Zhou Jiantao, et al. Stochastic memristive devices for computing and neuromorphic applications. Nanoscale, 2013, 5(13): 5872-5878.
[20]
35. Indiveri G, Linares-Barranco B, Legenstein R, et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology, 2013, 24(38, SI): 1-6.
[21]
36. Linares-Barranco B, Serrano-Gotarredona T. Memristance can explain Spike-Time-Dependent-Plasticity in neural synapses [EB/OL]. Nature Precedings, 2009 (2009-03-31) [2017-03-31]. http://hdl.handle.net/10101/npre.2009.3010.1.
[22]
37. Howard G D, Bull L, de Lacy Costello B. Evolving unipolar memristor spiking neural networks. Connection Science, 2015, 27(4): 397-416.
[23]
2. Maass W. Networks of spiking neurons: The third generation of neural network models. Neural Networks, 1997, 10(9): 1659-1671.
[24]
3. Ghoshdastidar S, Adeli H. Spiking neural networks. Int J Neural Syst, 2009, 19(4): 295-308.
[25]
4. 刘培龙. 基于FPGA的神经网络硬件实现的研究与设计. 成都: 电子科技大学, 2012.
[26]
5. Chua L O. Memristor–The missing circuit element. IEEE Transactions on Circuit Theory, 1971, 18(5): 507-519.
[27]
6. Williams R S. How we found the missing memristor. IEEE Spectrum, 2008, 45(12): 28-35.
[28]
7. Snider G S. Spike-timing-dependent learning in memristive nanodevices// NANOARCH'08 Proceedings of the 2008 IEEE International Symposium on Nanoscale Architectures. Washington: IEEE, 2008: 85-92.
[29]
8. Jo S H, Chang Ting, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10(4): 1297-1301.
[30]
14. Yu Shimeng, Wu Yi, Jeyasingh R, et al. An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation. IEEE Trans Electron Devices, 2011, 58(8, SI): 2729-2737.
[31]
15. Jerry M, Tsai W Y, Xie Baihua, et al. Phase transition oxide neuron for spiking neural networks//2016 74th Annual Device Research Conference (DRC). Newark: IEEE, 2016: 1-2.
17. Hebb D O. The organization of behavior: a neuropsychological theory// The organization of behavior: a neuropsychological theory. New York: John Wiley, Chapman & Hall, 1949: 74-76.
[34]
18. Indiveri G, Chicca E, Douglas R. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Transactions on Neural Networks, 2006, 17(1): 211-221.
[35]
19. Serrano-Gotarredona T, Masquelier T, Prodromakis T, et al. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front Neurosci, 2013, 7(7): 2.
24. Querlioz D, Bichler O, Gamrat C. Simulation of a memristor-based spiking neural network immune to device variations// The 2011 International Joint Conference on Neural Networks. San Jose: IEEE, 2011: 1775-1781.
[38]
25. Querlioz D, Bichler O, Dollfus P, et al. Immunity to device variations in a spiking neural network with memristive nanodevices. IEEE Trans Nanotechnol, 2013, 12(3): 288-295.
[39]
26. Serrano-Gotarredona T, Prodromakis T, Linares-Barranco B. A proposal for hybrid memristor-CMOS spiking neuromorphic learning systems. IEEE Circuits & Systems Magazine, 2013, 13(2): 74-88.