Associative memory realized by a reconfigurable memristive Hopfield neural network

Nature Communications

Volumn 6, Issue , 2015, Pages

  Hu, S G ^a   Liu, Y ^a   Liu, Z ^b   Chen, T P ^c   Wang, J J ^a   Yu, Q ^a   Deng, L J ^a   Yin, Y ^d   Hosaka, Sumio ^d  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^d GUNMA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

METAL; OXIDE;

ARTIFICIAL NEURAL NETWORK; MEMORY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; ASSOCIATIVE MEMORY; ELECTRIC CONDUCTANCE; ELECTRIC POTENTIAL; MEMRISTIVE HOPFIELD NETWORK; MODEL; NERVE CELL; RECALL; SCANNING ELECTRON MICROSCOPY; SEMICONDUCTOR; SYNAPSE; WAVEFORM; ALGORITHM; ANIMAL; AUTOMATED PATTERN RECOGNITION; CENTRAL NERVOUS SYSTEM; COMPUTER SIMULATION; LEARNING; MEMORY; PHYSIOLOGY;

ALGORITHMS; ANIMALS; ASSOCIATION LEARNING; CENTRAL NERVOUS SYSTEM; COMPUTER SIMULATION; MEMORY; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 84933060043     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms8522     Document Type: Article

References (37)

1. Mead, C. Neuromorphic electronic systems. Proc. IEEE. 78, 1629-1636 (1990
2. Von-Neumann, J. The principles of large-scale computing machines. IEEE Ann. Hist. Comput. 10, 243-256 (1988
3. Backus, J. Can programming be liberated from the von Neumann style?. A functional style and its algebra of programs. Commun. ACM. 21, 613-641 (1978
4. Furber, S. & Temple, S. Neural systems engineering. J. R. Soc. Interface 4, 193-206 (2007
5. Ahmad, Z., Mat Noor, R. A. & Zhang, J. Multiple neural networks modeling techniques in process control: a review. Asia-Pac. J. Chem. Eng. 4, 403-419 (2009
6. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. 79, 2554-2558 (1982
7. Hopfield, J. J. Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl Acad. Sci. 81, 3088-3092 (1984
8. Hopfield, J. J. & Tank, D. W. 'Neural' computation of decisions in optimization problems. Biol. Cybern. 52, 141-152 (1985
9. Hopfield, J. J. & Tank, D. W. Computing with neural circuits: a model. Science. 233, 625-633 (1986
10. Verleysen, M., Sirletti, B., Vandemeulebroecke, A. & Jespers, P. G. A highstorage capacity content-Addressable memory and its learning algorithm. IEEE Trans. Circuits Syst. 36, 762-766 (1989
11. Du, K.-L. & Swamy, M. (eds) in Neural Networks and Statistical Learning Ch. 6, 159-186 (Springer, 2014
12. Chua, L. Memristor-The missing circuit element. IEEE Trans. Circuit Theory. 18, 507-519 (1971
13. Chua, L. O. & Kang, S.-M. Memristive devices and systems. Proc. IEEE. 64, 209-223 (1976
14. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature. 453, 80-83 (2008
15. Jo, S. H, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297-1301 (2010
16. Li, Y, et al. Ultrafast synaptic events in a chalcogenide memristor. Sci. Rep. 3, 1619 (2013
17. Wang, Z. Q, et al. Synaptic learning and memory functions achieved using oxygen ion migration/diffusion in an amorphous InGaZnO memristor. Adv. Funct. Mater. 22, 2759-2765 (2012
18. 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
19. Kuzum, D., Yu, S. & Wong, H. S. P. Synaptic electronics: materials, devices and applications. Nanotechnology. 24, 382001 (2013
20. Hu, S. G, et al. Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor. Appl. Phys. Lett. 102, 183510 (2013
21. Krzysteczko, P., Münchenberger, J., Schäfers, M., Reiss, G. & Thomas, A. The memristive magnetic tunnel junction as a nanoscopic synapse-neuron system. Adv. Mater. 24, 762-766 (2012
22. Chang, T, et al. Synaptic behaviors and modeling of a metal oxide memristive device. Appl. Phys. A 102, 857-863 (2011
23. Seo, K, et al. Analog memory and spike-Timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device. Nanotechnology. 22, 254023 (2011
24. Yu, S. M., Wu, Y., Jeyasingh, R., Kuzum, D. G. & Wong, H. S. P. An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation. IEEE Trans. Electron Devices. 58, 2729-2737 (2011
25. Choi, S. J, et al. Synaptic behaviors of a single metal-oxide-metal resistive device. Appl. Phys. A 102, 1019-1025 (2011
26. Ziegler, M, et al. An electronic version of Pavlov's dog. Adv. Funct. Mater. 22, 2744-2749 (2012
27. Hu, S. G, et al. Design of an electronic synapse with spike time dependent plasticity based on resistive memory device. J. Appl. Phys. 113, 114502-114504 (2013
28. Pershin, Y. V. & Di Ventra, M. Experimental demonstration of associative memory with memristive neural networks. Neural Networks. 23, 881-886 (2010
29. 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
30. Park, S. et al.in Electron Devices Meeting (IEDM), 2013 IEEE Int. 25.6.1-25.6.4 (Washington, DC, USA, 2013
31. Eryilmaz, S. B, et al. Brain-like associative learning using a nanoscale nonvolatile phase change synaptic device array. Front. Neurosci. 8, 205 (2014
32. Burr, G. et al.in Electron Devices Meeting (IEDM), 2014 IEEE Int. 29.5.1-29.5.4 (San Francisco, CA, USA, 2014
33. Calka, P, et al. Chemical and structural properties of conducting nanofilaments in TiN/HfO2-based resistive switching structures. Nanotechnology. 24, 085706 (2013
34. Zhao, L, et al. Multi-level control of conductive nano-filament evolution in HfO2 ReRAM by pulse-Train operations. Nanoscale 6, 5698-5702 (2014
35. Yang, J. J. S., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13-24 (2013
36. Basheer, I. A. & Hajmeer, M. Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods. 43, 3-31 (2000
37. Rojas, R. Neural Networks: A Systematic Introduction (Springer, 1996