Review of the research of spiking neuron network based on memristor_Journal of Biomedical Engineering

Authors：

 XU Guizhi , YAO Linjing , LI Zikang

Department of Electrical Engineering, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China;

Corresponding?author：

XU Guizhi, Email: gzxu@hebut.edu.cn

Keywords：

artificial intelligence; spiking neuron networks; memristor; spike timing dependent plasticity mechanism

DOI：

10.7507/1001-5515.201703091

Video：

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The rapid development of artificial intelligence put forward higher requirements for the computational speed, resource consumption and the biological interpretation of computational neuroscience. Spiking neuron networks can carry a large amount of information, and realize the imitation of brain information processing. However, its hardware is an important way to realize its powerful computing ability, and it is also a challenging technical problem. The memristor currently is the electronic devices that functions closest to the neuron synapse, and able to respond to spike voltage in a highly similar spike timing dependent plasticity (STDP) mechanism with a biological brain, and has become a research hotspot to construct spiking neuron networks hardware circuit in recent years. Through consulting the relevant literature at home and abroad, this paper has made a thorough understanding and introduction to the research work of the spiking neuron networks based on the memristor in recent years.

Citation： XU Guizhi, YAO Linjing, LI Zikang. Review of the research of spiking neuron network based on memristor. Journal of Biomedical Engineering, 2018, 35(3): 475-480. doi: 10.7507/1001-5515.201703091 Copy

1.	徐波, 劉成林, 曾毅. 類腦智能研究現狀與發展思考. 中國科學院院刊, 2016, 31(7): 793-802.
2.	Maass W. Networks of spiking neurons: The third generation of neural network models. Neural Networks, 1997, 10(9): 1659-1671.
3.	Ghoshdastidar S, Adeli H. Spiking neural networks. Int J Neural Syst, 2009, 19(4): 295-308.
4.	劉培龍. 基于FPGA的神經網絡硬件實現的研究與設計. 成都: 電子科技大學, 2012.
5.	Chua L O. Memristor–The missing circuit element. IEEE Transactions on Circuit Theory, 1971, 18(5): 507-519.
6.	Williams R S. How we found the missing memristor. IEEE Spectrum, 2008, 45(12): 28-35.
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.
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.
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.
10.	Thomas A. Memristor-based neural networks. J Phys D Appl Phys, 2013, 46(9): 093001.
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.
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.
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.
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.
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.
16.	劉益春, 徐海陽, 王中強, 等. 基于憶阻器的神經突觸仿生器件研究. 科學, 2013, 65(2): 21-25.
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.
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.
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.
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.
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.
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.
23.	劉玉東, 王連明. 基于憶阻器的 spiking 神經網絡在圖像邊緣提取中的應用. 物理學報, 2014, 63(8): 34-40.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
34.	Gaba S, Sheridan P, Zhou Jiantao, et al. Stochastic memristive devices for computing and neuromorphic applications. Nanoscale, 2013, 5(13): 5872-5878.
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.
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.
37.	Howard G D, Bull L, de Lacy Costello B. Evolving unipolar memristor spiking neural networks. Connection Science, 2015, 27(4): 397-416.
38.	李傳東, 葛均輝, 田園. 脈沖神經網絡的憶阻器突觸聯想學習電路分析. 重慶大學學報, 2014, 37(7): 115-124.
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.

1. 徐波, 劉成林, 曾毅. 類腦智能研究現狀與發展思考. 中國科學院院刊, 2016, 31(7): 793-802.
2. Maass W. Networks of spiking neurons: The third generation of neural network models. Neural Networks, 1997, 10(9): 1659-1671.
3. Ghoshdastidar S, Adeli H. Spiking neural networks. Int J Neural Syst, 2009, 19(4): 295-308.
4. 劉培龍. 基于FPGA的神經網絡硬件實現的研究與設計. 成都: 電子科技大學, 2012.
5. Chua L O. Memristor–The missing circuit element. IEEE Transactions on Circuit Theory, 1971, 18(5): 507-519.
6. Williams R S. How we found the missing memristor. IEEE Spectrum, 2008, 45(12): 28-35.
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.
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.
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.
10. Thomas A. Memristor-based neural networks. J Phys D Appl Phys, 2013, 46(9): 093001.
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.
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.
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.
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.
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.
16. 劉益春, 徐海陽, 王中強, 等. 基于憶阻器的神經突觸仿生器件研究. 科學, 2013, 65(2): 21-25.
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.
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.
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.
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.
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.
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.
23. 劉玉東, 王連明. 基于憶阻器的 spiking 神經網絡在圖像邊緣提取中的應用. 物理學報, 2014, 63(8): 34-40.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
34. Gaba S, Sheridan P, Zhou Jiantao, et al. Stochastic memristive devices for computing and neuromorphic applications. Nanoscale, 2013, 5(13): 5872-5878.
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.
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.
37. Howard G D, Bull L, de Lacy Costello B. Evolving unipolar memristor spiking neural networks. Connection Science, 2015, 27(4): 397-416.
38. 李傳東, 葛均輝, 田園. 脈沖神經網絡的憶阻器突觸聯想學習電路分析. 重慶大學學報, 2014, 37(7): 115-124.
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.

Journal of Biomedical Engineering

Review of the research of spiking neuron network based on memristor

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