Associative learning of integrate-and-fire neurons with memristor-based synapses

Neural Processing Letters

Volumn 38, Issue 1, 2013, Pages 69-80

  Wen, Shiping ^a,b   Zeng, Zhigang ^a,b   Huang, Tingwen ^c  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Ministry of China   (China)

^c TEXAS A AND M UNIVERSITY AT QATAR   (Qatar)

Author keywords

Associative learning; Memristor; Neural network; Synaptic weight

Indexed keywords

ASSOCIATIVE LEARNING; INTEGRATE AND FIRES; INTEGRATE-AND-FIRE NEURONS; MEMRISTOR; POST-SYNAPTIC NEURONS; SYNAPTIC CONNECTIONS; SYNAPTIC PLASTICITY; SYNAPTIC WEIGHT;

INTEGRATED CIRCUIT MANUFACTURE; MEMRISTORS; NETWORKS (CIRCUITS); NEURONS; PASSIVE FILTERS; SPICE;

NEURAL NETWORKS;

EID: 84880507453     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-012-9263-8     Document Type: Article

References (53)

1. Jo S, Chang T, Ebong I, Bhadviya B, Mazumder P, Lu W (2010) Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett 10:1297-1301
2. Smith L (2006) Handbook of nature-inspired and innovative computing: integrating classical models with emerging technologies. Springer, New York
3. Ananthanarayanan R, Eser S, Simon H, Modha D (2009) In: Proceedings of IEEE/ACM conference high performance networking computing. Portland, OR, November 2009
4. Izhikevich E, Edelman G (2008) Large-scale model of mammalian thalamocortical systems. Proc Natl Acad Sci USA 105:3593-3598
5. Indiveri G, Chicca E, Douglas R (2006) A VLSI array of low-power spiking neurons and bistable synapses with spike? timing dependent plasticity. IEEE Trans Neural Netw 17:211-221
6. -(1999) The scientific American book of the brain. Scientifc American, New York.
7. Chua L, Yang L (1988) Cellular neural networks: theory. IEEE Trans Circuits Syst 35:1257-1272
8. Chua L, yang L (1988) Cellular neural networks: applications. IEEE Trans Circuits Syst II 35:1273-1290
9. Wen S, Zeng Z (2012) Dynamics analysis of a class of memristor-based recurrent networks with time-varying delays in the presence of strong external stimuli. Neural Process Lett 35:47-59
10. He H, Yan L, Tu J (2012) Guaranteed stabilization of time-varying delay cellular neural networks via Riccati inequality approach. Neural Process Lett 35:151-158
11. Su T, Huang M, Hou C (2010) Cellular neural networks for gray image noise cancellation based on a hybrid linear matrix inequaltiy and particle swarm optimization approach. Neural Process Lett 32:147-165
12. Sang Y, Yi Z, Zhou J (2010) Spatial point-data reduction using pulse coupled neural network. Neural Process Lett 32:11-29
13. Balasubramaniam P, Vembarasan V, Rakkiyappan R (2011) Leakage delays in T-S fuzzy cellular neural networks. Neural Process Lett 33:111-136
14. Li H, Liao X, Li C, Huang H, Li C (2011) Edge detection of noisy images based on cellular neural networks. Commun Nonlinear Sci Numer Simul 16:3746-3759
15. Li H, Liao X, Liao R (2012) A unified approach to chaos suppressing and inducing in a periodically forced family of nonlinear oscillators. IEEE Trans Circuits Syst I 59:784-795
16. He X, Li C, Shu Y (2012) Bogdanov-Takens bifurcation in a single inertial neuron model with delay. Neurocomput 89(15):193-201
17. He X, Li C, Huang T, Li C (2013) Codimension two bifurcation in a delayed neural network with unidirectional coupling. Nonlinear anal RWA 14(2):1191-1202
18. Wang H, Song Q, Duan C (2010) LMI criteria on exponential stability of BAM neural networks with both time-varying delays and general activation functions. Mathemat Comput Simul 81(4):837-850
19. Wang H, Song Q (2010) State estimation for neural networks with mixed interval time-varying delays. Neurocomputer 73(7):1281-1288
20. Li C, Li C, Huang T (2011) Exponential stability of impulsive high-order Hopfield-type neural networks with delays and reaction-diffusion. Int J Comput Math 88(15):3150-3162
21. Li C, Li C, Liao X, Huang T (2011) Impulsive effects on stability of high-order BAM neural networks with time delays. Neurocomputer 74(10):1541-1550
22. Huang T, Li C, Duan S et al (2012) Robust exponential stability of uncertain delayed neural networks With stochastic perturbation and impulse effects. IEEE Trans Neural Netw Learn Syst 23:866-875
23. Li C, Wu S, Feng G, Liao X (2011) Stabilizing effects of impulses in discrete-time delayed neural networks. IEEE Trans Neural Netw 22:323-329
24. Li C, Feng G (2008) Stabilizing effects of impulse in delayed BAM neural networks. IEEE Trans Circuits Syst II 55:1284-1288
25. Li H, Gao H, Shi H (2010) Passivity analysis for neural networks with discrete and distributed delays. IEEE Trans Neural Netw 22:1842-1847
26. Li H, Chen B, Zhou Q, Qian W (2009) Robust stability for uncertain delayed fuzzy hopfield neural networks with markovian jumping parameters. IEEE Trans Syst Man Cybern B Cybern 39:94-102
27. Roska T, Chua L (1993) The CNN universal machine: an analogic array computer. IEEE Trans Circuits Syst II 40:163-172
28. Zheng C, Zhang H, Wang Z (2010) Improved robust stability criteria for delayed cellular neural networks via the LMI approach. IEEE Trans Circuits Syst II Expr Briefs 57:41-45
29. Kim H, Pd Sah M, Yang C, Roska T, Chua L (2011) Neural synaptic weighting with a pulse-based memritor circuit. IEEE Trans Circuits Syst I 59:148-158
30. Strukov D, Snider G, Stewart D, Williams R (2008) The missing memristor found. Nature 453:80-83
31. Chua L (1971) Memristor-The missing circuit element. IEEE Trans Circuits Theory 18:507-519
32. Liu S, Douglas R (2004) Temporal coding in a silicon network of integrate-and-fire neurons. IEEE Trans Neural Netw 15:1305-1314
33. Chicca E, Badoni D, Dante V, D'Andreagiovanni M, Salina G, Fusi S, Del Giudice P (2003) A VLSI recurrent network of integrate-and fire neurons connected by plastic synapses with long term memory. IEEE Trans Neural Netw 14:1297-1307
34. Choi T, Shi B, Boahen K (2004) An on-off orientation selective address event representation image transceiver chip. IEEE Trans Circuits Syst I 51:342-353
35. Indiveri G (2001) A neuromorphic VLSI device for implementing 2-D selective attention systems. IEEE Trans Neural Netw 12:1455-1463
36. Di Ventra M, Pershin Y, Chua L (2009) Circuit elements with memory: memristor, memcapacitors and meminductors. Proc IEEE 97:1717-1724
37. Cantley K, Subramaniam A, Stiegler H, Chapman P, Vogel E (2011) Hebbian learning in spiking neural networks with nanocrystalline silicon TFTs and memristive synapse. IEEE Trans Nanotechnol 10:1066-1073
38. Cantley K, Subramaniam A, Stiegler H, Chapman P, Vogel E (2012) Neural learning circuits utilizing nano-crystalline silicon transistors and memristors. IEEE Trans Neural Netw Learn Syst 23:565-573
39. Snider G (2007) Self-organized computation with unreliable, memristive nanodevices. Nanotechnology 18:1-13
40. Sah M, Yang C, Kim H, Chua L (2012) A voltage mode memristor bridge synaptic circuit with memristor emulators. Sensors 12:3587-3604
41. Kim H, Sah M, Yang C, Cho S, Chua L (2012) Memristor bridge synapses. Proc IEEE. doi: 10.1109/jproc.2011.2166749
42. Linares-Barranco B, Serrano-Gotarredona T (2009) Exploiting memristance in adaptive asynchronous spiking neuromorphic nanotechnology systems. In 9th IEEE Conference on Nanotechnology, Genoa, Italy, 601-604
43. Di Pershin Y, Ventra M (2010) Experimental demonstration of associative memory with memristive neural networks. Neural Netw 23:881-886
44. Xia Q, Robinett W, Cumbie M, Banerjee N, Cardinali T, Yang J, Wu W, Li X, Tong W, Strukov D, Snider G, Medeiros-Ribeiro G, Williams R (2009) Memristor-CMOS hybrid integrated circuits for reconfigurable logic. Nano Lett 9:3640-3645
45. Wang X, Chen Y, Xi H, Li H, Dimitrov D (2009) Spintronic memristor through spin-torque-induced magnetization motion. IEEE Electron Device Lett 30:294-297
46. Yang J, Picket M, Li X, Ohlberg A, Stewart D, Williams R (2008) Memristive switching mechanism for metal/oxide/metal nanodevices. Nature Nanotechnol 3:429-433
47. Ho Y, Huang G, Li P (2011) Dyanmical properties and design analysis for nonvolatile memristor memories. IEEE Trans Circuits Syst I 58:724-736
48. Joglekar Y, Wolf S (2009) The elusive memristor: properties of basic electrical circuits. Eur J Phys 30:661-675
49. Pickett M, Strukov D, Borghetti J, Yang J, Snider G, Williams R (2009) Switching dynamics in titanium dioxide memristive devices. J Appl Phys 106:074508-074508-6
50. Mead C (1989) Analog VLSI and neural systems. Addison-Wesley, Reading
51. Diorio C, Hasler P, Minch B, Mead C (1996) A single-transistor silicon synapse. IEEE Trans Electron Devices 43:1972-1980
52. Chua L, Kang S (1976) Memristive devices and systems. Proc IEEE 64:209-223
53. Chen L, Li C, Wang X, Duan S (2012) Associate learning and correcting in a memristive neural network. Neural Comput Appl. doi: 10.1007/s00521-012-0868-7