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SCIENTIA SINICA Informationis, Volume 47, Issue 3: 385-400(2017) https://doi.org/10.1360/N112016-00061

Neural networks based on doublet generator synapses and its \\applications in image processing}{Neural networks based on doublet generator synapses and its applications in image processing

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  • ReceivedMar 24, 2016
  • AcceptedApr 21, 2016
  • PublishedNov 28, 2016

Abstract

Memristor is a nanoscale device with memory and similar synaptic properties. The memristor bridge synaptic circuit, which has the advantages of a simple structure and precise control, can be applied to artificial neural networks, ultra-large-scale integrated circuits, image processing, pattern recognition, etc. However, traditional memristor bridge synaptic circuitry will cause some errors in the synaptic simulation process due to the accumulation of flux caused by unipolar pulses. In this paper, we propose a new synaptic bridge circuit, which effectively overcomes the memristance drifting problem of traditional bridge circuits. Then we apply the new synaptic bridge circuit to a synaptic neural network; the application in image processing has advantages that are more obvious. The feasibility of the structure was verified through simulation experiments, confirming the efficient biomimetic properties of neural networks based on doublet generator memristor bridge synapses. In addition, its higher degree of integration and the ability to replace templates more easily make it more effective in solving real-time complex intelligent problems.


Funded by

国家自然科学基金(61372139)

国家自然科学基金(61571372)

国家自然科学基金(61101233)

国家自然科学基金(60972155)

新世纪优秀人才支持计划(教技函[2013]47号)

教育部``春晖计划''科研项目(z2011148)

中央高校基本科研业务费专项资金(XDJK2016A001)

中央高校基本科研业务费专项资金(XDJK- 2014A009)

留学人员科技活动项目择优资助经费(渝人社办[2012]186号)

重庆市高等学校优秀人才支持计划(批准\\号 渝教人[2011]65号)


References

[1] Damasio A R. The Scientific American Book of the Brain. New York: Globe Pequot, 2001. Google Scholar

[2] Chua L O. Memristor-the missing circuit element. IEEE Trans Circ Theory, 1971, 18: 507-519 CrossRef Google Scholar

[3] Strukov D B, Snider G S, Stewart D R, et al. The missing memristor found. Nature, 2008, 453: 80-83 CrossRef Google Scholar

[4] Afifi A, Ayatollahi A, Raissi F. STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks. IEICE Electron Express, 2009, 6: 148-153 CrossRef Google Scholar

[5] Laiho M, Lehtonen E, Russel A, et al. Memristive synapses are becoming reality. The Neuromorphic Engineer, 2010, doi: 10.2417/1201011.003396. Google Scholar

[6] Afifi A, Ayatollahi A, Raissi F. Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits. In: Proceedings of European Conference on Circuit Theory and Design, Antalya, 2009. 563-566. Google Scholar

[7] Ventra M D, Pershin Y V, Chua L O. Circuit elements with memory: memristors, memcapacitors, and meminductors. Proc IEEE, 2009, 97: 1717-1724 CrossRef Google Scholar

[8] Cantley K D, Subramaniam A, Stiegler H J, et al. Hebbian learning in spiking neural networks with nanocrystalline silicon TFTs and memristive synapses. IEEE Trans Nanotech, 2011, 10: 1066-1073 CrossRef Google Scholar

[9] Li T S, Duan S K, Liu J, et al. A spintronic memristor-based neural network with radial basis function for robotic manipulator control implementation. IEEE Trans Syst Man Cybernetics Syst, 2015, 46: 582-588. Google Scholar

[10] Wang H M, Duan S K, Huang T W, et al. Exponential stability of complex-valued memristive recurrent neural networks. IEEE Trans Neural Netw Learn Syst, 2015, 99: 1-6. Google Scholar

[11] Duan S K, Wang H M, Wang L D, et al. Impulsive effects and stability analysis on memristive neural networks with variable delays. IEEE Trans Neural Netw Learn Syst, in press. doi: 10.1109/TNNLS,2015.2497319. Google Scholar

[12] Wang L D, Duan M T, Duan S K, et al. Neural networks based on STDP rules and memristor bridge synapses with applications in image processing. Sci Sin Inform, 2014, 44: 920-930 [王丽丹, 段美涛, 段书凯, 等. 基于STDP规则和忆阻桥突触的神经网络及图像处理. 中国科学: 信息科学, 2014, 44: 920-930]. Google Scholar

[13] Duan S K, Hu X F, Dong Z K, et al. Memristor-based cellular nonlinear/neural network: design, analysis, and applications. IEEE Trans Neural Netw Learn Syst, 2015, 26: 1202-1213 CrossRef Google Scholar

[14] Meng F Y, Duan S K, Wang L D, et al. An improved WO$_{x}$ memristor mo del with synapse characteristic analysis. Acta Phys Sin, 2015, 64: 148501 [孟凡一, 段书凯, 王丽丹, 等. 一种改进的WO$_{x}$忆阻器模型及其突触特性分析. 物理学报, 2015, 64: 148501]. Google Scholar

[15] Jo S H, Chang T, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10: 1297-1301 CrossRef Google Scholar

[16] Corinto F, Ascoli A, Kim Y S, et al. Cellular Nonlinear Networks With Memristor Synapses. Berlin: Springer International Publishing, 2014. Google Scholar

[17] Adhikari S P, Kim H. A circuit-based learning architecture for multilayer neural networks with memristor bridge synapses. IEEE Trans Circ Syst I Regular Papers, 2015, 62: 215-223 CrossRef Google Scholar

[18] Chua L O, Yang L. Cellular neural networks: theory. IEEE Trans Circ Syst, 1988, 35: 1257-1272 CrossRef Google Scholar

[19] Chua L O, Yang L. Cellular neural networks: applications. IEEE Trans Circ Syst, 1988, 35: 1273-1290 CrossRef Google Scholar

[20] Kim H, Sah P, Yang C, et al. Memristor bridge synapses. Proc IEEE, 2012, 100: 2061-2070 CrossRef Google Scholar

[21] Kim H, Sah M P, Yang C, et al. Neural synaptic weighting with a pulse-based memristor circuit. IEEE Trans Circ Syst I Regular Papers, 2011, 59: 148-158. Google Scholar

[22] Adhikari S P, Kim H, Kong B S, et al. Memristance drift avoidance with charge bouncing for memristor-based nonvolatile memories. J Korean Phys Soc, 2012, 61: 1418-1421 CrossRef Google Scholar

[23] Li Q J. The discussion of the application of the definition of derivative. Sci Tech Inform, 2012, 33: 690-691 [李庆娟. 浅谈导数定义的应用. 科学信息, 2012, 33: 690-691]. Google Scholar

[24] Hu X F, Duan S K, Wang L D, et al. Memristive crossbar array with applications in image processing. Sci China Inf Sci, 2012, 55: 461-472 CrossRef Google Scholar

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