Research Progress of Memristor-based Neuromorphic Synapses

CHEN Xinman; ZHONG Zhijian; YUE Zhixiu; ZHU Jun; GAO Fangliang; SHI Yanli; ZHANG Yong

doi:10.6054/j.jscnun.2022079

Journal of South China Normal University (Natural Science Edition) > 2022 > 54(6): 1-15. > DOI: 10.6054/j.jscnun.2022079

CHEN Xinman, ZHONG Zhijian, YUE Zhixiu, ZHU Jun, GAO Fangliang, SHI Yanli, ZHANG Yong. Research Progress of Memristor-based Neuromorphic Synapses[J]. Journal of South China Normal University (Natural Science Edition), 2022, 54(6): 1-15. DOI: 10.6054/j.jscnun.2022079

Citation:

PDF (33309 KB)

Research Progress of Memristor-based Neuromorphic Synapses

1.
College of Semiconductor Science and Technology, South China Normal University, Foshan 528225, China
2.
Zhongshan Torch Technical and Vocational College, Zhongshan 528436, China
3.
Library of South China Agricultural University, Guangzhou 510642, China

More Information

Received Date: June 15, 2022
Available Online: February 13, 2023

Graphical Abstract

Abstract

Abstract

The efficient, fast and accurate information transmission in the large-scale neural network in the brain is the exact origin that the brain can control the complex life activities of humans and animals and enable organisms to survive in the changeable natural environment. As an important medium for information transmission between neurons, the synapses ensure the efficient operation of neural networks. Therefore, to build electronic synapses with synaptic functions is one essential way to study bionic systems and brain-like neural networks. Researchers have previously tried to simulate synaptic functions with various electronic devices, among which memristor has become one good candidate to build neuromorphic synapses due to its unique device structure and memory characteristics. The researches of memristor-based synapses in recent years are comprehensively summarized in this article, including the synaptic plasticity, metaplasticity, non-associative learning, associative learning and other functions. It also summarizes the application, problems and challenges in artificial neural networks, as well as the research prospects of memristor-based synapses.
- memristor,
- synapse,
- plasticity,
- neural network

FullText(HTML)

References (94)

References

[1]	LAI Q, ZHANG L, LI Z, et al. Ionic/electronic hybrid materials integrated in a synaptic transistor with signal processing and learning functions[J]. Advanced Materials, 2010, 22(22): 2448-2453. doi: 10.1002/adma.201000282
[2]	OHNO T, HASEGAWA T, TSURUOKA T, et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses[J]. Nature Materials, 2011, 10(8): 591-595. doi: 10.1038/nmat3054
[3]	JOSBERGER E E, DENG Y, SUN W, et al. Two-terminal protonic devices with synaptic-like short-term depression and device memory[J]. Advanced Materials, 2014, 26(29): 4986-4990. doi: 10.1002/adma.201400320
[4]	CHANG T, JO S, LU W. Short-term memory to long-term memory transition in a nanoscale memristor[J]. ACS Nano, 2011, 5(9): 7669-7676. doi: 10.1021/nn202983n
[5]	RACHMUTH G, POON C. Transistor analogs of emergent iono-neuronal dynamics[J]. HFSP Journal, 2008, 2(3): 156-166. doi: 10.2976/1.2905393
[6]	LIU Y H, ZHU L Q, FENG P, et al. Freestanding artificial synapses based on laterally proton-coupled transistors on chitosan membranes[J]. Advanced Materials, 2015, 27(37): 5599-5604. doi: 10.1002/adma.201502719
[7]	LONT J B, GUGGENBUHL W. Analog CMOS implementation of a multilayer perceptron with nonlinear synapses[J]. IEEE Trans Neural Network, 1992, 3(3): 457-465. doi: 10.1109/72.129418
[8]	CHUA L. Memristor-the missing circuit element[J]. IEEE Transactions on Circuit Theory, 1971, 18(5): 507-519. doi: 10.1109/TCT.1971.1083337
[9]	WILLIAMS R S, STRUKOV D B, SNIDER G S, et al. The missing memristor found[J]. Nature, 2008, 453: 80-83. doi: 10.1038/nature06932
[10]	HE W, HUANG K, NING N, et al. Enabling an integrated rate-temporal learning scheme on memristor[J]. Scienti-fic Reports, 2014, 4: 4755/1-6.
[11]	LI Y, ZHONG Y, ZHANG J, et al. Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems[J]. Scientific Reports, 2014, 4: 4906/1-7.
[12]	MILANO G, LUEBBEN M, MA Z, et al. Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities[J]. Nature Communications, 2018, 9(1): 5110-5151. doi: 10.1038/s41467-018-07561-8
[13]	ZHU L Q, WAN C J, GUO L Q, et al. Artificial synapse network on inorganic proton conductor for neuromorphic systems[J]. Nature Communication, 2014, 5: 3158/1-7.
[14]	XU W, CHO H, KIM Y, et al. Artificial synapses: organometal halide perovskite artificial synapses[J]. Advanced Materials, 2016, 28(28): 6019/1-7.
[15]	LI B, LIU Y, WAN C, et al. Mediating short-term plasti-city in an artificial memristive synapse by the orientation of silica mesopores[J]. Advanced Materials, 2018, 30(16): 1706395/1-7.
[16]	NAJEM J S, TAYLOR G J, WEISS R J, et al. Memristive ion channel-doped biomembranes as synaptic mimics[J]. ACS Nano, 2018, 12(5): 4702-4711. doi: 10.1021/acsnano.8b01282
[17]	ZHANG Y, ZHONG S, SONG L, et al. Emulating dynamic synaptic plasticity over broad timescales with memristive device[J]. Applied Physics Letters, 2018, 113(20): 203102/1-5.
[18]	WANG Z, JOSHI S, SAVEL S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing[J]. Nature Materials, 2017, 16(1): 101-108. doi: 10.1038/nmat4756
[19]	NAJEM J S, TAYLOR G J, WEISS R J, et al. Memristive ion channel-doped biomembranes as synaptic mimics[J]. ACS Nano, 2018, 12(5): 4702-4711. doi: 10.1021/acsnano.8b01282
[20]	MALENKA R C, BEAR M F. LTP and LTD: an embarrassment of riches[J]. Neuron, 2004, 44(1): 5-21. doi: 10.1016/j.neuron.2004.09.012
[21]	DANG B, WU Q, SONG F, et al. A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors[J]. Nanoscale, 2018, 10(43): 20089-20095. doi: 10.1039/C8NR07442A
[22]	KIM S, KIM H, WANG S, et al. Analog synaptic behavior of a silicon nitride memristor[J]. ACS Applied Materials & Interfaces, 2017, 9(46): 40420-40427.
[23]	WANG Z, XU H, LI X, et al. Synaptic learning and memory functions achieved using oxygen ion migration/diffusion in an amorphous InGaZnO memristor[J]. Advanced Functional Materials, 2012, 22(13): 2759-2765. doi: 10.1002/adfm.201103148
[24]	JO S, CHANG T, EBONG I, et al. Nanoscale memristor device as synapse in neuromorphic systems[J]. Nano Letters, 2010, 10(4): 1297-1301. doi: 10.1021/nl904092h
[25]	SHEN J X, SHANG D S, CHAI Y S, et al. Mimickingsynaptic plasticity and neural network using memtranstors[J]. Advanced Materials, 2018, 30(12): 1706717/1-8.
[26]	BLISS T, GARDNER-MEDWIN A. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path[J]. The Journal of Physiology, 1973, 232(2): 331-356. doi: 10.1113/jphysiol.1973.sp010273
[27]	BEAR M F, MALENKA R C. Synaptic plasticity: LTP and LTD[J]. Current Opinion in Neurobiology, 1994, 4(3): 389-399. doi: 10.1016/0959-4388(94)90101-5
[28]	YANG C S, SHANG D S, LIU N, et al. A synaptic transistor based on quasi-2D molybdenum oxide[J]. Advanced Materials, 2017, 29(27): 1700906/1-10.
[29]	REN Y, HU L, MAO J, et al. Phosphorene nano-heterostructure based memristors with broadband response synaptic plasticity[J]. Journal of Materials Chemistry C, 2018, 6(35): 9383-9393. doi: 10.1039/C8TC03089H
[30]	ZHOU L, MAO J Y, REN Y, et al. Biological spiking synapse constructed from solution processed bimetal core-shell nanoparticle based composites[J]. Small, 2018, 14(28): 1800288/1-10.
[31]	XIAO Z, HUANG J. Energy-efficient hybrid perovskite memristors and synaptic devices[J]. Advanced Electronic Materials, 2016, 2(7): 1600100/1-8.
[32]	MILO V, PEDRETTI G, CARBONI R, et al. Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity[C]. 2016 IEEE International Electron Devices Meeting (IEDM). San Francisco: IEEE, 2016.
[33]	LIU Q, WANG L, YANG J, et al. Fusion ofimage storage and operation based on Ag-chalcogenide memristor with synaptic plasticity[J]. Journal of Circuits Systems & Computers, 2017: 1750161/1-17.
[34]	LI Y, ZHONG Y, ZHANG J, et al. Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems[J]. Scientific Reports, 2014, 4: 4906/1-7.
[35]	BI G, POO M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type[J]. Journal of Neuroscience, 1998, 18(24): 10464-10472. doi: 10.1523/JNEUROSCI.18-24-10464.1998
[36]	MARKRAM H, LUBKE J, FROTSCHER M, et al. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs[J]. Science, 1997, 275: 213-215. doi: 10.1126/science.275.5297.213
[37]	WILLIAMSON A, SCHUMANN L, HILLER L, et al. Synaptic behavior and STDP of asymmetric nanoscale memristors in biohybrid systems[J]. Nanoscale, 2013, 5(16): 7297-7303. doi: 10.1039/c3nr01834b
[38]	SONG S, ABBOTT L F, MILLER K D. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity[J]. Nature Neuroscience, 2000, 3(9): 919-926. doi: 10.1038/78829
[39]	WU Y, YU S, WONG H S P, et al. AlO_x-based resistive switching device with gradual resistance modulation for neuromorphic device application[C]. 2012 4th IEEE International Memory Workshop. Milan: IEEE, 2012.
[40]	LASHKARE S, PANWAR N, KUMBHARE P, et al. PCMO-based RRAM and NPN bipolar selector as synapse for energy efficient STDP[J]. IEEE Electron Device Letters, 2017, 38(9): 1212-1215. doi: 10.1109/LED.2017.2723503
[41]	PANWAR N, RAJENDRAN B, GANGULY U. Arbitrary Spike Time Dependent Plasticity (STDP) in memristor by analog waveform engineering[J]. IEEE Electron Device Letters, 2017, 38(6): 740-743. doi: 10.1109/LED.2017.2696023
[42]	SONG S, MILLER K D, ABBOTT L F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity[J]. Nature Neuroscience, 2000, 3(9): 919-926. doi: 10.1038/78829
[43]	WITTENBERG G M, WANG S S H. Malleability of spike-timing-dependent plasticity at the CA3-CA1 synapse[J]. Journal of Neuroscience, 2006, 26(24): 6610-6617. doi: 10.1523/JNEUROSCI.5388-05.2006
[44]	CASSENAER S, LAURENT G. Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts[J]. Nature, 2007, 448: 709-713. doi: 10.1038/nature05973
[45]	FIETE I R, SEN N W, WANG C Z, et al. Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity[J]. Neuron, 2010, 65(4): 563-576. doi: 10.1016/j.neuron.2010.02.003
[46]	KUZUM D, JEYASINGH R G, LEE B, et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing[J]. Nano Letters, 2012, 12(5): 2179-2186. doi: 10.1021/nl201040y
[47]	ABRAHAM W C. Metaplasticity: tuning synapses and networks for plasticity[J]. Nature Reviews Neuroscience, 2008, 9(5): 387-399. doi: 10.1038/nrn2356
[48]	CHRISTIE B R, ABRAHAM W C. Priming of associative long-term depression in the dentate gyrus by theta frequency synaptic activity[J]. Neuron, 1992, 9(1): 79-84. doi: 10.1016/0896-6273(92)90222-Y
[49]	STAUBLI U, LYNCH G. Stable depression of potentiated synaptic responses in the hippocampus with 1-5 Hz sti-mulation[J]. Brain Research, 1990, 513(1): 113-118. doi: 10.1016/0006-8993(90)91096-Y
[50]	FUJII S, SAITO K, MIYAKAWA H, et al. Reversal of long-term potentiation (depotentiation) induced by tetanus stimulation of the input to CA1 neurons of guinea pig hip-pocampal slices[J]. Brain Research, 1991, 555(1): 112-122. doi: 10.1016/0006-8993(91)90867-U
[51]	HULME S R, JONES O D, ABRAHAM W C. Emerging roles of metaplasticity in behaviour and disease[J]. Trends in Neurosciences, 2013, 36(6): 353-362. doi: 10.1016/j.tins.2013.03.007
[52]	DISTERHOFT J F, OH M M. Learning, aging and intrinsic neuronal plasticity[J]. Trends Neurosciences, 2006, 29(10): 587-599. doi: 10.1016/j.tins.2006.08.005
[53]	LINDEN D J, ZHANG W. The other side of the engram: experience-driven changes in neuronal intrinsic excitabi-lity[J]. Nature Reviews Neuroscience, 2003, 4(11): 885-900. doi: 10.1038/nrn1248
[54]	ECKERT M J, ABRAHAM W C. Physiological effects of enriched environment exposure and LTP induction in the hippocampus in vivo do not transfer faithfully to in vitro slices[J]. Learning & Memory, 2010, 17(10): 480-484.
[55]	MALIK R, CHATTARJI S. Enhanced intrinsic excitability and EPSP-spike coupling accompany enriched environment-induced facilitation of LTP in hippocampal CA1 pyramidal neurons[J]. Journal of Neurophysiology, 2012, 107(5): 1366-1378. doi: 10.1152/jn.01009.2011
[56]	TAN Z H, YANG R, TERABE K, et al. Synaptic metaplasticity realized in oxide memristive devices[J]. Advanced Materials, 2016, 28(2): 377-384. doi: 10.1002/adma.201503575
[57]	LEE T, HWANG H, WOO J, et al. Synaptic plasticity and metaplasticity of biological synapse realized in a KNbO₃ memristor for application to artificial synapse[J]. ACS Applied Materials & Interfaces, 2018, 10(30): 25673-25682.
[58]	ZHONG Z, JIANG Z, HUANG J, et al. "Stateful" threshold switching for neuromorphic[J]. Nanoscale, 2022, 14(14): 5010-5021.
[59]	ZHANG C, YE W B, ZHOU K, et al. Bioinspired artificial sensory nerve based on nafion memristor[J]. Advanced Functional Materials, 2019, 29(20): 1808783/1-10.
[60]	GE J, ZHANG S, LIU Z, et al. Flexible artificial nociceptor using a biopolymer-based forming-free memristor[J]. Nanoscale, 2019, 11(14): 6591-6601. doi: 10.1039/C8NR08721K
[61]	YANG X, FANG Y, YU Z, et al. Nonassociative learning implementation by a single memristor-based multi-terminal synaptic device[J]. Nanoscale, 2016, 8(45): 18897-18904. doi: 10.1039/C6NR04142F
[62]	WANG Z, HONG Q, WANG X. Memristive circuit design of emotional generation and evolution based on skin-like sensory processor[J]. IEEE Transactions on Biomedical Circuits and Systems, 2019, 13(4): 631-644. doi: 10.1109/TBCAS.2019.2923055
[63]	YOON J H, WANG Z, KIM K M, et al. An artificial nociceptor based on a diffusive memristor[J]. Nature Communications, 2018, 9(1): 417-419. doi: 10.1038/s41467-017-02572-3
[64]	COHEN T E, KAPLAN S W, KANDEL E R, et al. A simplified preparation for relating cellular events to behavior: mechanisms contributing to habituation, dishabituation, and sensitization of the Aplysia Gill-Withdrawal reflex[J]. Journal of Neuroscience, 1997, 17(8): 2886-2899. doi: 10.1523/JNEUROSCI.17-08-02886.1997
[65]	THOMPSON R F, SPENCER W A. Habituation: a model phenomenon for the study of neuronal substrates of beha-vior[J]. Psychological Review, 1966, 73(1): 16-43. doi: 10.1037/h0022681
[66]	FRUHSTORFER H. Habituation and dishabituation of the human vertex response[J]. Electroencephalography and Clinical Neurophysiology, 1971, 30(4): 306-312. doi: 10.1016/0013-4694(71)90113-1
[67]	CAREW T J, CASTELLUCCI V F, KANDEL E R. An analysis of dishabituation and sensitization of the gill-withdrawal reflex in Aplysia[J]. The International Journal of Neuroscience, 1971, 2(2): 79-88. doi: 10.3109/00207457109146995
[68]	ZHAO B, XIAO M, SHEN D, et al. Heterogeneous stimuli induced nonassociative learning behavior in ZnO nanowire memristor[J]. Nanotechnology, 2019, 31(12): 125201/1-23.
[69]	PERSHIN Y V, DI VENTRA M. Experimental demonstration of associative memory with memristive neural networks[J]. Neural Networks, 2010, 23(7): 881-886. doi: 10.1016/j.neunet.2010.05.001
[70]	ZIEGLER M, SONI R, PATELCZYK T, et al. An electronic version of Pavlov's dog[J]. Advanced Functional Materials, 2012, 22(13): 2744-2749. doi: 10.1002/adfm.201200244
[71]	BICHLER O, ZHAO W, ALIBART F, et al. Pavlov's dog associative learning demonstrated on synaptic-like organic transistors[J]. Neural Computation, 2013, 25(2): 549-566. doi: 10.1162/NECO_a_00377
[72]	WAN C, ZHOU J, SHI Y, et al. Classical conditioning mimicked in junctionless IZO electric-double-layer thin-film transistors[J]. IEEE Electron Device Letters, 2014, 35(3): 414-416. doi: 10.1109/LED.2014.2299796
[73]	WU C, KIM T W, GUO T, et al. Mimicking classical conditioning based on a single flexible memristor[J]. Advanced Materials, 2017, 29(10): 1602890/1-10.
[74]	ZHONG Y, GAO X, XU J, et al. Selective UV-gating organic memtransistors with modulable levels of synaptic plasticity[J]. Advanced Electronic Materials, 2019, 6(2): 1900955/1-7.
[75]	HU D, YANG R, JIANG L, et al. Memristive synapses with photoelectric plasticity realized in ZnO_1-_x/AlO_y heterojunction[J]. ACS Applied Materials & Interfaces, 2018, 10(7): 6463-6470.
[76]	ZHOU F, ZHOU Z, CHEN J, et al. Optoelectronic resistive random access memory for neuromorphic vision sensors[J]. Nature Nanotechnology, 2019, 14(8): 776-782. doi: 10.1038/s41565-019-0501-3
[77]	CHEN S, LOU Z, CHEN D, et al. An artificial flexible visual memory system based on an UV-motivated memristor[J]. Advanced Materials, 2018, 30(7): 1705400/1-9.
[78]	GAO S, LIU G, YANG H, et al. An oxide schottky junction artificial optoelectronic synapse[J]. ACS nano, 2019, 13(2): 2634-2642. doi: 10.1021/acsnano.9b00340
[79]	ZHOU F, ZHOU Z, CHEN J, et al. Optoelectronic resistive random access memory for neuromorphic vision sensors[J]. Nature Nanotechnology, 2019, 14(8): 776-782. doi: 10.1038/s41565-019-0501-3
[80]	LI H, JIANG X, YE W, et al. Fully photon modulated heterostructure for neuromorphic computing[J]. Nano Energy, 2019, 65: 104000/1-37.
[81]	MAIER P, HARTMANN F, DIAS M, et al. Light sensitive memristor with bi-directional and wavelength-dependent conductance control[J]. Applied Physics Letters, 2016, 109(2): 23501/1-6.
[82]	SHERIDAN P, CAI F, DU C, et al. Sparse coding with memristor networks[J]. Nature Nanotechnology, 2017, 12(8): 784-789. doi: 10.1038/nnano.2017.83
[83]	PREZIOSO M, MERRIKH-BAYAT F, HOSKINS B D, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors[J]. Nature, 2015, 521: 61-64. doi: 10.1038/nature14441
[84]	PARK S, CHU M, KIN J, et al. Electronic system with memristive synapses for pattern recognition[J]. Scientific Reports, 2015, 5(1): 10123/1-9.
[85]	SHERIDANP, DU C, LU W. Feature extraction using memristor networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(11): 2327-2336. doi: 10.1109/TNNLS.2015.2482220
[86]	CHOI S, SHIN J, LEE J, et al. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks[J]. Nano Letters, 2017, 17(5): 3113-3118. doi: 10.1021/acs.nanolett.7b00552
[87]	JEONG Y, LEE J, MOON J, et al. K-means data clustering with memristor networks[J]. Nano Letters, 2018, 18(7): 4447-4453. doi: 10.1021/acs.nanolett.8b01526
[88]	PARK S, CHU M, KIN J, et al. Electronic system with memristive synapses for pattern recognition[J]. Scientific Reports, 2015, 5(1): 10123/1-9.
[89]	CHU M, KIM B, PARK S, et al. Neuromorphic hardware system for visual pattern recognition with memristor array and CMOS neuron[J]. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2410-2419. doi: 10.1109/TIE.2014.2356439
[90]	YAO P, WU H, GAO B, et al. Face classification using electronic synapses[J]. Nature Communications, 2017, 8(1): 15199/1-8.
[91]	AL-SHEDIVAT M, NAOUS R, GAUWENBERGHS G, et al. Memristors empower spiking neurons with stochasticity[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2015, 5(2): 242-253. doi: 10.1109/JETCAS.2015.2435512
[92]	ZHANG T, XU X, CAI Y, et al. Memristive devices and networks for brain-inspired computing[J]. Physica Status Solidi-Rapid Research Letters, 2019, 13(8): 1900029/1-21.
[93]	WANG C, HE W, TONG Y, et al. Memristive devices with highly repeatable analog states boosted by graphene quantum dots[J]. Small, 2017, 13(20): 1603435/1-8.
[94]	JIANG B C, NAM Y, KOO B J, et al. Memristive logic-in-memory integrated circuits for energy-efficient flexible electronics[J]. Advanced Functional Materials, 2018, 28(2): 1704725/1-10.

Cited By

Cited by

Periodical cited type(3)

1.	林俤，吴易明，杨森，张垠，赵铭姝. 忆阻神经网络自适应滑模控制及其应用. 红外与激光工程. 2024(06): 203-210 .
2.	王鹤鹏，李志军. 忆阻耦合的三异质神经元网络及其图像加密应用. 湖南理工学院学报(自然科学版). 2024(02): 23-30 .
3.	吴建新，江玮，钟祎，刁宇欣. 几种基于忆阻器的触发器电路设计. 实验技术与管理. 2024(07): 15-21 .

Other cited types(8)

Get Citation

PDF

XML

Article views (1803) PDF downloads (442) Cited by(11)

Turn off MathJax

Article Contents

Abstract

References

Research Progress of Memristor-based Neuromorphic Synapses

Abstract

References

Cited by

Periodical cited type(3)

Other cited types(8)

Catalog

Export File

Citation

Format

Content