Neuromorphic character recognition system with two PCMO memristors as a synapse

IEEE Transactions on Industrial Electronics

Volumn 61, Issue 6, 2014, Pages 2933-2941

  Sheri, Ahmad Muqeem ^a   Hwang, Hyunsang ^b   Jeon, Moongu ^a   Lee, Byung Geun ^a  

^a Gwangju Institute of Science and Technology (GIST)   (South Korea)

^b Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Character recognition; memristors; neural classifier; neuromorphic

Indexed keywords

CHARACTER RECOGNITION; MANGANESE OXIDE; NEURAL NETWORKS; PASSIVE FILTERS;

CHARACTER RECOGNITION SYSTEM; DESIGN AND SIMULATION; HIGH RESISTANCE; LOW RESISTANCE; NEURAL ARCHITECTURES; NEURAL CLASSIFIERS; NEUROMORPHIC; SYNAPTIC CONNECTIONS;

MEMRISTORS;

EID: 84891766688     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2013.2275966     Document Type: Article

References (23)

1. S. Huang and K. K. Tan, Intelligent friction modeling and compensation using neural network approximations, IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3342-3349, Aug. 2012.
2. H. Zhuang, K.-S. Low, and W.-Y. Yau, Multichannel pulse-coupledneural- network-based color image segmentation for object detection, IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3299-3308, Aug. 2012.
3. Y. Pan and J. Wang, Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks, IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3089-3101, Aug. 2012.
4. B. K. Bose, Neural network applications in power electronics and motor drives - An introduction and perspective, IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 14-33, Feb. 2007. (Pubitemid 46444002)
5. G. Snider, Self-organized computation with unreliable, memristive nanodevices, Nanotechnology, vol. 18, no. 36, p. 365 202, Sep. 2007. [Online]. Available: Http://dx.doi.org/10.1088/0957-4484/18/36/365202
6. S. Jo, T. Chang, I. Ebong, B. Bhadviya, P. Mazumder, and W. Lu, Nanoscale memristor device as synapse in neuromorphic systems, Nano Lett., vol. 10, no. 4, pp. 1297-1301, 2010. [Online]. Available: Http://dx. doi.org/10.1021/ nl904092h
7. C. Zamarreño-Ramos, L. Camuñas-Mesa, J. Pérez- Carrasco, T. Masquelier, T. Serrano-Gotarredona, and B. Linares-Barranco, On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex, Frontiers Neurosci., vol. 5, 2011. [Online]. Available: Http://dx.doi.org/10.3389/fnins.2011.00026
8. A. Afifi, A. Ayatollahi, and F. Raissi, Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits, in Proc. ECCTD, 2009, pp. 563-566.
9. D. Querlioz, O. Bichler, and C. Gamrat, Simulation of a memristor-based spiking neural network immune to device variations, in Proc. IJCNN, Aug. 5-31, 2011, pp. 1775-1781.
10. H. Kim, M. Sah, C. Yang, T. Roska, and L. Chua, Neural synaptic weighting with a pulse-based memristor circuit, IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 59, no. 1, pp. 148-158, Jan. 2012.
11. K. Seo, I. Kim, S. Jung, M. Jo, S. Park, J. Park, J. Shin, K. Biju, J. Kong, K. Lee, B. Lee, and H. Hwang, Analog memory and spike-timingdependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device, Nanotechnology, vol. 22, no. 25, p. 254 023, Jun. 2011. [Online]. Available: Http://dx.doi.org/10.1088/0957-4484/22/25/254023
12. M. Suri, O. Bichler, D. Querlioz, O. Cueto, L. Perniola, V. Sousa, D. Vuillaume, C. Gamrat, and B. DeSalvo, Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction, in IEDM Tech. Dig., Dec. 2011, pp. 4.4.1-4.4.4.
13. S. Cohen-Cory, The developing synapse: Construction and modulation of synaptic structures and circuits, Science, vol. 298, no. 5594, pp. 770-776, Oct. 2002. [Online]. Available: Http://dx.doi.org/10.1126/science. 1075510 (Pubitemid 35231520)
14. A. Burkitt, A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input, Biol. Cybern., vol. 95, no. 1, pp. 1-19, Jul. 2006. [Online]. Available: Http://dx.doi.org/10.1007/s00422-006-0068-6 (Pubitemid 43999784)
15. S. Park, S. Jung, M. Siddik, M. Jo, J. Lee, J. Park, W. Lee, S. Kim, S. Sadaf, and X. Liu, Memristive switching behavior in pr0. 7ca0. 3mno3 by incorporating an oxygen deficient layer, Phys. Stat. Sol. (RRL)-Rapid Res. Lett., vol. 5, no. 10/11, pp. 409-411, Nov. 2011. [Online]. Available: Http://dx.doi.org/10.1002/pssr.201190023
16. M. Jo, D. Seong, S. Kim, J. Lee, W. Lee, J. Park, S. Park, S. Jung, J. Shin, and D. Lee, Novel cross-point resistive switching memory with self-formed Schottky barrier, in Proc. Symp. VLSIT, 2010, pp. 53-54.
17. J. Yang, M. Pickett, X. Li, D. A. Ohlberg, D. Stewart, and R. Williams, Memristive switching mechanism for metal/oxide/metal nanodevices, Nat. Nanotechnol., vol. 3, no. 7, pp. 429-433, Jul. 2008. [Online]. Available: Http://dx.doi.org/10.1038/nnano.2008.160
18. S. Jo, K. Kim, T. Chang, S. Gaba, and W. Lu, Si memristive devices applied to memory and neuromorphic circuits, in Proc. IEEE ISCAS, 2010, pp. 13-16.
19. G. Wang, B. Grone, D. Colas, L. Appelbaum, and P. Mourrain, Synaptic plasticity in sleep: Learning, homeostasis and disease, Trends Neurosci., vol. 34, no. 9, pp. 452-463, Aug. 2011. [Online]. Available: Http://dx.doi. org/10.1016/j.tins.2011.07.005
20. S. Panzeri, E. Rolls, F. Battaglia, and R. Lavis, Speed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons, Network, vol. 12, no. 4, pp. 423-440, Nov. 2001. (Pubitemid 33625156)
21. P. Livi and G. Indiveri, A current-mode conductance-based silicon neuron for address-event neuromorphic systems, in Proc. IEEE ISCAS, 2009, pp. 2898-2901.
22. A. Joubert, B. Belhadj, and R. Héliot, A robust and compact 65 nm LIF analog neuron for computational purposes, in Proc. IEEE 9th Int. NEWCAS, 2011, pp. 9-12.
23. E. Simoncelli and B. Olshausen, Natural image statistics and neural representation, Ann. Rev. Neurosci., vol. 24, pp. 1193-1216, Mar. 2001. [Online]. Available: Http://dx.doi.org/10.1146/annurev.neuro.24.1.1193 (Pubitemid 32695258)