Short-term plasticity and long-term potentiation mimicked in single inorganic synapses

A synaptic model of memory: long-term potentiation in the hippocampus

Bliss, T. V. P., & Collingridge, G. L. (1993) · Nature · doi:10.1038/361031a0 · Cited by 11,566 · see citing works

Principles of neural science

(1982) · Trends in Neurosciences · doi:10.1016/0166-2236(82)90076-5 · Cited by 9,148 · see citing works

The Psychology of Learning and Motivation: Advances in Research and Theory

(2010) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/c2009-0-62209-1 · Cited by 6,035 · see citing works

Nanoionics-based resistive switching memories

Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923 · see citing works

Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type

Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744 · see citing works

Nanoscale Memristor Device as Synapse in Neuromorphic Systems

Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · see citing works

Memristive switching mechanism for metal/oxide/metal nanodevices

Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.160 · Cited by 2,987 · see citing works

Memory: A Contribution to Experimental Psychology

Ebbinghaus, H. (2013) · Annals of Neurosciences · doi:10.5214/ans.0972.7531.200408 · Cited by 2,827 · see citing works

Synaptic Plasticity and Memory: An Evaluation of the Hypothesis

Martin, S. J., Grimwood, P. D., & Morris, R. G. M. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.649 · Cited by 2,794 · see citing works

Cellular neural networks: applications

Chua, L. O., & Yang, L. (1988) · IEEE Transactions on Circuits and Systems · doi:10.1109/31.7601 · Cited by 2,392 · see citing works

Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit

Tank, D. W., & Hopfield, J. J. (1986) · IEEE Transactions on Circuits and Systems · doi:10.1109/tcs.1986.1085953 · Cited by 2,196 · see citing works

Learning induces long-term potentiation in the hippocampus

Whitlock, J. R., Heynen, A. J., Shuler, M. G., & Bear, M. F. (2006) · Science · doi:10.1126/science.1128134 · Cited by 1,913 · see citing works

Memory: A contribution to experimental psychology.

Ebbinghaus, H. (1913) · doi:10.1037/10011-000 · Cited by 1,645 · see citing works

Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit

Hahnloser, R. H. R., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J., & Seung, H. S. (2000) · Nature · doi:10.1038/35016072 · Cited by 1,293 · see citing works

Quantized conductance atomic switch

Terabe, K., Hasegawa, T., Nakayama, T., & Aono, M. (2005) · Nature · doi:10.1038/nature03190 · Cited by 1,211 · see citing works

Chaotic neural networks

Aihara, K., Takabe, T., & Toyoda, M. (1990) · Physics Letters A · doi:10.1016/0375-9601(90)90136-c · Cited by 1,179 · see citing works

A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity

Indiveri, G., Chicca, E., & Douglas, R. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.860850 · Cited by 1,009 · see citing works

Chaotic Neural Networks

Binder, M. D., Hirokawa, N., & Windhorst, U. (2008) · Springer eBooks · doi:10.1007/978-3-540-29678-2_965 · Cited by 928 · see citing works

One hundred years of forgetting: A quantitative description of retention.

Rubin, D. C., & Wenzel, A. E. (1996) · Psychological Review · doi:10.1037/0033-295x.103.4.734 · Cited by 820 · see citing works

A silicon neuron

Mahowald, M. A., & Douglas, R. (1991) · Nature · doi:10.1038/354515a0 · Cited by 491 · see citing works

A single-transistor silicon synapse

Diorio, C., Hasler, P., Minch, A., & Mead, C. A. (1996) · IEEE Transactions on Electron Devices · doi:10.1109/16.543035 · Cited by 360 · see citing works

Compact silicon neuron circuit with spiking and bursting behaviour

Wijekoon, J. H. B., & Dudek, P. (2008) · Neural Networks · doi:10.1016/j.neunet.2007.12.037 · Cited by 359 · see citing works

Ionic/Electronic Hybrid Materials Integrated in a Synaptic Transistor with Signal Processing and Learning Functions

Lai, Q., Zhang, L., Li, Z., Stickle, W. F., Williams, R. S., & Chen, Y. (2010) · Advanced Materials · doi:10.1002/adma.201000282 · Cited by 333 · see citing works

Learning Abilities Achieved by a Single Solid‐State Atomic Switch

Hasegawa, T., Ohno, T., Terabe, K., Tsuruoka, T., Nakayama, T., Gimzewski, J. K., & Aono, M. (2010) · Advanced Materials · doi:10.1002/adma.200903680 · Cited by 328 · see citing works

Real-Time <i>In Situ</i> HRTEM-Resolved Resistance Switching of Ag₂S Nanoscale Ionic Conductor

Xu, Z., Bando, Y., Wang, W., Bai, X., & Golberg, D. (2010) · ACS Nano · doi:10.1021/nn100483a · Cited by 266 · see citing works

Self-organized computation with unreliable, memristive nanodevices

Snider, G. S. (2007) · Nanotechnology · doi:10.1088/0957-4484/18/36/365202 · Cited by 264 · see citing works

Neuromorphic Analogue VLSI

Douglas, R. J., Mahowald, M., & Mead, C. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001351 · Cited by 252 · see citing works

The Psychology of Learning and Motivation: Advances in Research and Theory: Vol 41

Conway, M., Whitecross, S., Pleydell-Pearce, C. W., & Sharpe, H. (2002) · Academic Press eBooks · Cited by 223 · see citing works

Quantum Point Contacts

van Houten, H., & Beenakker, C. W. J. (1996) · Physics Today · doi:10.1063/1.881503 · Cited by 189 · see citing works

Long-term potentiation in the hippocampus

Voronin, L. L. (1983) · Neuroscience · doi:10.1016/0306-4522(83)90099-4 · Cited by 169 · see citing works

Proposal of Adaptive-Learning Neuron Circuits with Ferroelectric Analog-Memory Weights

Ishiwara, H. (1993) · Japanese Journal of Applied Physics · doi:10.1143/jjap.32.442 · Cited by 157 · see citing works

Neuromorphic implementation of orientation hypercolumns

Choi, T. Y. W., Merolla, P. A., Arthur, J. V., Boahen, K. A., & Shi, B. E. (2005) · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · doi:10.1109/tcsi.2005.849136 · Cited by 119 · see citing works

Solid-state thin-film memistor for electronic neural networks

Thakoor, S., Moopenn, A., Daud, T., & Thakoor, A. P. (1990) · Journal of Applied Physics · doi:10.1063/1.345390 · Cited by 88 · see citing works

SYNAPSE—A neurocomputer that synthesizes neural algorithms on a parallel systolic engine

Ramacher, U. (1992) · Journal of Parallel and Distributed Computing · doi:10.1016/0743-7315(92)90070-4 · Cited by 80 · see citing works

Forgetting

Brown, G. D. A. (. D. A. )., & Lewandowsky, S. (2010) · Psychology Press eBooks · doi:10.4324/9780203851647 · Cited by 63 · see citing works

CrossNets: possible neuromorphic networks based on nanoscale components

Türel, Ö., & Likharev, K. K. (2003) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.223 · Cited by 59 · see citing works

Memristive operations demonstrated by gap-type atomic switches

Hasegawa, T., Nayak, A., Ohno, T., Terabe, K., Tsuruoka, T., Gimzewski, J. K., & Aono, M. (2011) · Applied Physics A · doi:10.1007/s00339-011-6317-0 · Cited by 45 · see citing works

Boltzmann machine neuron circuit using single-electron tunneling

Akazawa, M., & Amemiya, Y. (1997) · Applied Physics Letters · doi:10.1063/1.118329 · Cited by 40 · see citing works

Electrochemical investigation of the α/ß-phase transition of silver sulfide

Wysk, H. (1997) · Solid State Ionics · doi:10.1016/s0167-2738(96)00611-x · Cited by 6 · see citing works

A million spiking-neuron integrated circuit with a scalable communication network and interface

Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Chen, G., Nakamura, Y., et al. (2014) · Science · doi:10.1126/science.1254642 · Cited by 4,137 · see citing works

Memristive devices for computing

Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.240 · Cited by 3,885 · see citing works

Training and operation of an integrated neuromorphic network based on metal-oxide memristors

Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001 · see citing works

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358 · see citing works

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing.

van de Burgt, Y., Lubberman, E., Fuller, E. J., Keene, S. T., Faria, G. C., Agarwal, S., Marinella, M. J., Alec Talin, A., & Salleo, A. (2017) · Nature Materials · doi:10.1038/nmat4856 · Cited by 1,872 · see citing works

Memristive crossbar arrays for brain-inspired computing

Xia, Q., & Yang, J. J. (2019) · Nature Materials · doi:10.1038/s41563-019-0291-x · Cited by 1,800 · see citing works

Recent progress in resistive random access memories: Materials, switching mechanisms, and performance

Pan, F., Gao, S., Chen, C., Song, C., & Zeng, F. (2014) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2014.06.002 · Cited by 1,399 · see citing works

Synaptic electronics: materials, devices and applications

Kuzum, D., Yu, S., & Wong, H. ‐. P. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/382001 · Cited by 1,257 · see citing works

Resistive switching materials for information processing

Wang, Z., Wu, H., Burr, G. W., Hwang, C. S., Wang, K. L., Xia, Q., & Yang, J. J. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0159-3 · Cited by 1,214 · see citing works

Physics for neuromorphic computing

Marković, D., Mizrahi, F. A., Querlioz, D., & Grollier, J. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-0208-2 · Cited by 1,200 · see citing works

Neuro-Inspired Computing With Emerging Nonvolatile Memorys

Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170 · see citing works

Neuromorphic computing using non-volatile memory

Burr, G. W., Shelby, R. M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K. R., Ishii, M., Narayanan, P., Fumarola, A., et al. (2016) · Advances in Physics X · doi:10.1080/23746149.2016.1259585 · Cited by 1,158 · see citing works

Stochastic phase-change neurons

Tůma, T., Pantazi, A., Gallo, M. L., Sebastian, A., & Eleftheriou, E. S. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2016.70 · Cited by 1,107 · see citing works

Organic electronics for neuromorphic computing

van de Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. G., & Salleo, A. (2018) · Nature Electronics · doi:10.1038/s41928-018-0103-3 · Cited by 1,110 · see citing works

Fully memristive neural networks for pattern classification with unsupervised learning

Wang, Z., Joshi, S., Savel’ev, S., Song, W., Midya, R., Li, Y., Rao, M., Yan, P., Asapu, S., Zhuo, Y., et al. (2018) · Nature Electronics · doi:10.1038/s41928-018-0023-2 · Cited by 1,106 · see citing works

Memory and Information Processing in Neuromorphic Systems

Indiveri, G., & Liu, S. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2444094 · Cited by 1,045 · see citing works

Reservoir computing using dynamic memristors for temporal information processing

Du, C., Cai, F., Zidan, M. A., Ma, W., Lee, S. H., & Lu, W. D. (2017) · Nature Communications · doi:10.1038/s41467-017-02337-y · Cited by 1,028 · see citing works

Face classification using electronic synapses

Yao, P., Wu, H., Gao, B., Eryilmaz, S. B., Huang, X., Zhang, W., Zhang, Q., Deng, N., Shi, L., Wong, H.-S. P., et al. (2017) · Nature Communications · doi:10.1038/ncomms15199 · Cited by 909 · see citing works

Neuromorphic computing with multi-memristive synapses

Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications · doi:10.1038/s41467-018-04933-y · Cited by 903 · see citing works

Artificial synapse network on inorganic proton conductor for neuromorphic systems

Zhu, L. Q., Wan, C., Guo, L. Q., Shi, Y., & Wan, Q. C. (2014) · Nature Communications · doi:10.1038/ncomms4158 · Cited by 852 · see citing works

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., et al. (2019) · Advanced Materials · doi:10.1002/adma.201902761 · Cited by 842 · see citing works

Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor

Wang, Z., Xu, H. Y., Li, X., Yu, H., Liu, Y., & Zhu, X. J. (2012) · Advanced Functional Materials · doi:10.1002/adfm.201103148 · Cited by 816 · see citing works

A comprehensive review on emerging artificial neuromorphic devices

Zhu, J., Zhang, T., Yang, Y., & Huang, R. (2020) · Applied Physics Reviews · doi:10.1063/1.5118217 · Cited by 763 · see citing works

Artificial optic-neural synapse for colored and color-mixed pattern recognition

Seo, S., Jo, S.-H., Kim, S., Shim, J., Oh, S., Kim, J.-H., Heo, K., Choi, J.-W., Choi, C., Oh, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07572-5 · Cited by 731 · see citing works

Sub-nanosecond switching of a tantalum oxide memristor

Torrezan, A. C., Strachan, J. P., Medeiros-Ribeiro, G., & Williams, R. S. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/48/485203 · Cited by 701 · see citing works

Recent Advances in Transistor‐Based Artificial Synapses

Dai, S., Zhao, Y., Wang, Y., Zhang, J., Fang, L., Jin, S., Shao, Y., & Huang, J. D. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201903700 · Cited by 675 · see citing works

Embedded Devices for Neuromorphic Time-Series Assessment

Mazumder, A. N., Hosseini, M., & Mohsenin, T. (2022) · Research Explorer (The University of Manchester) · doi:10.13016/m2ksy0-wxr3 · Cited by 662 · see citing works

Pattern classification by memristive crossbar circuits using ex situ and in situ training

Alibart, F., Zamanidoost, E., & Strukov, D. B. (2013) · Nature Communications · doi:10.1038/ncomms3072 · Cited by 642 · see citing works

Electronic synapses made of layered two-dimensional materials

Shi, Y., Liang, X., Yuan, B., Chen, V., Li, H., Hui, F., Yu, Z., Yuan, F., Pop, E., Wong, H. ‐. P., et al. (2018) · Nature Electronics · doi:10.1038/s41928-018-0118-9 · Cited by 634 · see citing works

Sparse coding with memristor networks

Sheridan, P. M., Cai, F., Du, C., Ma, W., Zhang, Z., & Lu, W. D. (2017) · Nature Nanotechnology · doi:10.1038/nnano.2017.83 · Cited by 619 · see citing works

Nanoscale cation motion in TaOx, HfOx and TiOx memristive systems

Wedig, A., Lübben, M., Cho, D., Moors, M., Skaja, K., Rana, V., Hasegawa, T., Adepalli, K. K., Yildiz, B., Waser, R. M., et al. (2015) · Nature Nanotechnology · doi:10.1038/nnano.2015.221 · Cited by 612 · see citing works

Experimental Demonstration of a Second-Order Memristor and Its Ability to Biorealistically Implement Synaptic Plasticity

Kim, S., Du, C., Sheridan, P., Ma, W., Choi, S., & Lu, W. D. (2015) · Nano Letters · doi:10.1021/acs.nanolett.5b00697 · Cited by 603 · see citing works

Nanobatteries in redox-based resistive switches require extension of memristor theory

Valov, I., Linn, E., Tappertzhofen, S., Schmelzer, S., van den Hurk, J., Lentz, F., & Waser, R. (2013) · Nature Communications · doi:10.1038/ncomms2784 · Cited by 587 · see citing works

Neuromorphic Functions in PEDOT:PSS Organic Electrochemical Transistors

Gkoupidenis, P., Schaefer, N., Garlan, B., & Malliaras, G. G. (2015) · Advanced Materials · doi:10.1002/adma.201503674 · Cited by 577 · see citing works

Organic core-sheath nanowire artificial synapses with femtojoule energy consumption

Xu, W., Min, S., Hwang, H., & Lee, T. (2016) · Science Advances · doi:10.1126/sciadv.1501326 · Cited by 573 · see citing works

Brain‐Inspired Photonic Neuromorphic Devices using Photodynamic Amorphous Oxide Semiconductors and their Persistent Photoconductivity

Lee, M., Lee, W., Choi, S., Jo, J., Kim, J., Park, S. K., & Kim, Y. (2017) · Advanced Materials · doi:10.1002/adma.201700951 · Cited by 568 · see citing works

Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems

Chicca, E., Stefanini, F., Bartolozzi, C., & Indiveri, G. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2313954 · Cited by 544 · see citing works

A correlated nickelate synaptic transistor

Shi, J. Z., Ha, S. D., Zhou, Y., Schoofs, F., & Ramanathan, S. (2013) · Nature Communications · doi:10.1038/ncomms3676 · Cited by 544 · see citing works

Emerging Memory Devices for Neuromorphic Computing

Upadhyay, N. K., Jiang, H., Wang, Z., Asapu, S., Xia, Q., & Yang, J. J. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201800589 · Cited by 542 · see citing works

Nanoarchitectonics for Dynamic Functional Materials from Atomic‐/Molecular‐Level Manipulation to Macroscopic Action

Ariga, K., Li, J., Fei, J., Ji, Q., & Hill, J. P. (2015) · Advanced Materials · doi:10.1002/adma.201502545 · Cited by 541 · see citing works

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Park, H., Lee, Y., Kim, N., Seo, D., Go, G., & Lee, T. (2019) · Advanced Materials · doi:10.1002/adma.201903558 · Cited by 541 · see citing works

Stretchable organic optoelectronic sensorimotor synapse

Lee, Y., Oh, J. Y., Xu, W., Kim, O., Kim, T. R., Kang, J., Kim, Y., Son, D., Tok, J. B. ‐., Park, M. J., et al. (2018) · Science Advances · doi:10.1126/sciadv.aat7387 · Cited by 536 · see citing works

STDP and STDP variations with memristors for spiking neuromorphic learning systems

Serrano‐Gotarredona, T., Masquelier, T., Prodromakis, T., Indiveri, G., & Linares-Barranco, B. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00002 · Cited by 531 · see citing works

A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation

Yu, S., Gao, B., Fang, Z., Yu, H. Y., Kang, J., & Wong, H. ‐. P. (2013) · Advanced Materials · doi:10.1002/adma.201203680 · Cited by 526 · see citing works

An artificial nociceptor based on a diffusive memristor

Yoon, J. H., Wang, Z., Kim, K. M., Wu, H., Ravichandran, V., Xia, Q., Hwang, C. S., & Yang, J. J. (2018) · Nature Communications · doi:10.1038/s41467-017-02572-3 · Cited by 524 · see citing works

Ion Gated Synaptic Transistors Based on 2D van der Waals Crystals with Tunable Diffusive Dynamics

Zhu, J., Yang, Y., Jia, R., Liang, Z., Zhu, W., Rehman, Z. U., Bao, L., Zhang, X., Cai, Y., Song, L., et al. (2018) · Advanced Materials · doi:10.1002/adma.201800195 · Cited by 498 · see citing works

All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing

Yang, C., Shang, D., Liu, N., Fuller, E. J., Agrawal, S., Talin, A. A., Li, Y., Shen, B., & Sun, Y. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201804170 · Cited by 491 · see citing works

Organometal Halide Perovskite Artificial Synapses

Xu, W., Cho, H., Kim, Y., Kim, Y., Wolf, C., Park, C. G., & Lee, T. (2016) · Advanced Materials · doi:10.1002/adma.201506363 · Cited by 451 · see citing works

A Ferroelectric/Electrochemical Modulated Organic Synapse for Ultraflexible, Artificial Visual‐Perception System

Wang, H., Zhao, Q., Ni, Z., Li, Q., Liu, H., Yang, Y., Wang, L., Yang, R., Guo, Y., Hu, W., et al. (2018) · Advanced Materials · doi:10.1002/adma.201803961 · Cited by 445 · see citing works

Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics

Du, C., Ma, W., Chang, T., Sheridan, P., & Lu, W. D. (2015) · Advanced Functional Materials · doi:10.1002/adfm.201501427 · Cited by 444 · see citing works

Artificial Synapses Emulated by an Electrolyte‐Gated Tungsten‐Oxide Transistor

Yang, J., Ge, C., Du, J., Huang, H., He, M., Wang, C., Lu, H., Yang, G., & Jin, K. (2018) · Advanced Materials · doi:10.1002/adma.201801548 · Cited by 444 · see citing works

Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing

Hou, Y., Yi, L., Zhang, Z., Li, J., Qi, D.-H., Chen, X., Wang, J.-J., Yao, B., Yu, M., Lu, T., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c08921 · Cited by 442 · see citing works

Memristor with Ag‐Cluster‐Doped TiO 2 Films as Artificial Synapse for Neuroinspired Computing

Yan, X., Zhao, J., Liu, S., Zhou, Z., Liu, Q., Chen, J., & Liu, X. (2017) · Advanced Functional Materials · doi:10.1002/adfm.201705320 · Cited by 443 · see citing works

An Artificial Flexible Visual Memory System Based on an UV‐Motivated Memristor

Chen, S., Lou, Z., Chen, D., & Shen, G. (2018) · Advanced Materials · doi:10.1002/adma.201705400 · Cited by 437 · see citing works

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

Choi, S., Yang, J., & Wang, G. (2020) · Advances in Materials · doi:10.1002/adma.202004659 · Cited by 369 · see citing works

Ultrafast Synaptic Events in a Chalcogenide Memristor

Li, Y., Zhong, Y., Xu, L., Zhang, J., Xu, X., Sun, H., & Miao, X. (2013) · Scientific Reports · doi:10.1038/srep01619 · Cited by 423 · see citing works

An Artificial Sensory Neuron with Tactile Perceptual Learning

Wan, C., Chen, G., Fu, Y., Wang, M., Matsuhisa, N., Pan, S., Pan, L., Yang, H., Wan, Q., Zhu, L., et al. (2018) · Advanced Materials · doi:10.1002/adma.201801291 · Cited by 418 · see citing works

Memristors for Energy‐Efficient New Computing Paradigms

Jeong, D. S., Kim, K. M., Kim, S., Choi, B. J., & Hwang, C. S. (2016) · Advanced Electronic Materials · doi:10.1002/aelm.201600090 · Cited by 413 · see citing works

Energy‐Efficient Hybrid Perovskite Memristors and Synaptic Devices

Xiao, Z., & Huang, J. (2016) · Advanced Electronic Materials · doi:10.1002/aelm.201600100 · Cited by 413 · see citing works

Optoelectronic Synaptic Devices for Neuromorphic Computing

Wang, Y., Yin, L., Huang, W., Li, Y., Huang, S., Zhu, Y., Yang, D., & Pi, X. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000099 · Cited by 407 · see citing works

Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials

Park, Y., & Lee, J. (2017) · ACS Nano · doi:10.1021/acsnano.7b03347 · Cited by 393 · see citing works

Atomic Switch: Atom/Ion Movement Controlled Devices for Beyond Von‐Neumann Computers

Hasegawa, T., Terabe, K., Tsuruoka, T., & Aono, M. (2011) · Advanced Materials · doi:10.1002/adma.201102597 · Cited by 391 · see citing works

Neuromorphic functions with a polyelectrolyte-confined fluidic memristor

Xiong, T., Li, C., He, X., Xie, B., Zong, J., Jiang, Y., Ma, W., Wu, F., Fei, J., Yu, P., et al. (2023) · Science · doi:10.1126/science.adc9150 · Cited by 391 · see citing works

Bioinspired mechano-photonic artificial synapse based on graphene/MoS 2 heterostructure

Yu, J., Yang, X., Gao, G., Xiong, Y., Wang, Y., Han, J., Chen, Y., Zhang, H., Sun, Q., & Wang, Z. L. (2021) · Science Advances · doi:10.1126/sciadv.abd9117 · Cited by 390 · see citing works

Brain-inspired computing with memristors: Challenges in devices, circuits, and systems

Zhang, Y., Wang, Z., Zhu, J., Yang, Y., Rao, M., Song, W., Zhuo, Y., Zhang, X., Cui, M., Shen, L., et al. (2020) · Applied Physics Reviews · doi:10.1063/1.5124027 · Cited by 382 · see citing works

Anatomy of Ag/Hafnia‐Based Selectors with 10 10 Nonlinearity

Midya, R., Wang, Z., Zhang, J., Savel'ev, S. E., Li, C., Rao, M., Jang, M. H., Joshi, S., Jiang, H., Lin, P., et al. (2017) · Advanced Materials · doi:10.1002/adma.201604457 · Cited by 381 · see citing works

Extremely Low Operating Current Resistive Memory Based on Exfoliated 2D Perovskite Single Crystals for Neuromorphic Computing

Tian, H., Zhao, L., Wang, X., Yeh, Y., Yao, N., Rand, B. P., & Ren, T.-L. (2017) · ACS Nano · doi:10.1021/acsnano.7b05726 · Cited by 373 · see citing works

Reinforcement learning with analogue memristor arrays

Wang, Z., Li, C., Song, W., Rao, M., Belkin, D., Li, Y., Yan, P., Jiang, H., Lin, P., Hu, M., et al. (2019) · Nature Electronics · doi:10.1038/s41928-019-0221-6 · Cited by 371 · see citing works

Semiconductor Quantum Dots for Memories and Neuromorphic Computing Systems

Lv, Z., Wang, Y., Chen, J., Wang, J., Zhou, Y., & Han, S. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00730 · Cited by 367 · see citing works

Beyond von‐Neumann Computing with Nanoscale Phase‐Change Memory Devices

Wright, C. D., Hosseini, P., & Diosdado, J. A. V. (2012) · Advanced Functional Materials · doi:10.1002/adfm.201202383 · Cited by 363 · see citing works

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Wang, Z., Rao, M., Midya, R., Joshi, S., Jiang, H., Lin, P., Song, W., Asapu, S., Zhuo, Y., Li, C., et al. (2017) · Advanced Functional Materials · doi:10.1002/adfm.201704862 · Cited by 362 · see citing works

Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

John, R. A., Liu, F., Chien, N. A., Kulkarni, M. R., Zhu, C., Fu, Q., Basu, A., Liu, Z., & Mathews, N. (2018) · Advanced Materials · doi:10.1002/adma.201800220 · Cited by 361 · see citing works

Neuromorphic Engineering: From Biological to Spike‐Based Hardware Nervous Systems

Yang, J., Wang, R., Ren, Y., Mao, J., Wang, Z., Zhou, Y., & Han, S. (2020) · Advanced Materials · doi:10.1002/adma.202003610 · Cited by 353 · see citing works

Recent Progress in Three‐Terminal Artificial Synapses: From Device to System

Han, H., Yu, H., Wei, H., Gong, J., & Xu, W. (2019) · Small · doi:10.1002/smll.201900695 · Cited by 350 · see citing works

Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems

Li, Y., Zhong, Y., Zhang, J., Xu, L., Wang, Q., Sun, H., Tong, H., Cheng, X., & Miao, X. (2014) · Scientific Reports · doi:10.1038/srep04906 · Cited by 344 · see citing works

Anisotropic Black Phosphorus Synaptic Device for Neuromorphic Applications

Tian, H., Guo, Q., Xie, Y., Zhao, H., Cheng, L., Cha, J. J., Xia, F., & Wang, H. (2016) · Advanced Materials · doi:10.1002/adma.201600166 · Cited by 345 · see citing works

Artificial Sensory Memory

Wan, C., Cai, P., Wang, M., Qian, Y., Huang, W., & Chen, X. (2019) · Advanced Materials · doi:10.1002/adma.201902434 · Cited by 344 · see citing works

Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system

Choi, C., Leem, J., Kim, M., Taqieddin, A., Cho, C., Cho, K. W., Lee, G. J., Seung, H., Bae, H. J., Song, Y. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19806-6 · Cited by 341 · see citing works

Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks

Ielmini, D. (2018) · Microelectronic Engineering · doi:10.1016/j.mee.2018.01.009 · Cited by 338 · see citing works

In situ detection of hydrogen-induced phase transitions in individual palladium nanocrystals

Baldi, A., Narayan, T. C., Koh, A. L., & Dionne, J. A. (2014) · Nature Materials · doi:10.1038/nmat4086 · Cited by 337 · see citing works

Artificial Optoelectronic Synapses Based on Ferroelectric Field-Effect Enabled 2D Transition Metal Dichalcogenide Memristive Transistors

Luo, Z., Xia, X., Yang, M., Wilson, N. R., Gruverman, A. L., & Alexe, M. (2019) · ACS Nano · doi:10.1021/acsnano.9b07687 · Cited by 335 · see citing works

Optically Stimulated Artificial Synapse Based on Layered Black Phosphorus

Ahmed, T., Kuriakose, S., Mayes, E. L. H., Ramanathan, R., Bansal, V., Bhaskaran, M. C., Sriram, S., & Walia, S. (2019) · Small · doi:10.1002/smll.201900966 · Cited by 334 · see citing works

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Shen, Z., Zhao, C., Qi, Y., Xu, W., Liu, Y., Mitrović, I. Z., Li, Y., & Zhao, C. (2020) · Nanomaterials · doi:10.3390/nano10081437 · Cited by 333 · see citing works

2D MoS 2 Neuromorphic Devices for Brain‐Like Computational Systems

Jiang, J., Guo, J., Wan, X., Yang, Y., Xie, H., Niu, D., Yang, J., He, J., Gao, Y., & Wan, Q. (2017) · Small · doi:10.1002/smll.201700933 · Cited by 328 · see citing works

Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices

Wang, W., Wang, M., Ambrosi, E., Bricalli, A., Laudato, M., Sun, Z., Chen, X., & Ielmini, D. (2019) · Nature Communications · doi:10.1038/s41467-018-07979-0 · Cited by 328 · see citing works

Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability

Wu, C., Kim, T. W., Choi, H. Y., Strukov, D. B., & Yang, J. J. (2017) · Nature Communications · doi:10.1038/s41467-017-00803-1 · Cited by 321 · see citing works

Emerging neuromorphic devices

Ielmini, D., & Ambrogio, S. (2019) · Nanotechnology · doi:10.1088/1361-6528/ab554b · Cited by 273 · see citing works

Magnetic skyrmion-based synaptic devices

Huang, Y., Kang, W., Zhang, X., Zhou, Y., & Zhao, W. S. (2017) · Nanotechnology · doi:10.1088/1361-6528/aa5838 · Cited by 314 · see citing works

Fully Light‐Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

Ahmed, T., Tahir, M., Low, M. X., Ren, Y., Tawfik, S. A., Mayes, E. L. H., Kuriakose, S., Nawaz, S., Spencer, M. J. S., Chen, H., et al. (2020) · Advanced Materials · doi:10.1002/adma.202004207 · Cited by 319 · see citing works

Towards Oxide Electronics: a Roadmap

Coll, M., Fontcuberta, J., Althammer, M., Bibès, M., Boschker, H., Calleja, A., Cheng, G., Cuoco, M., Dittmann, R., Dkhil, B., et al. (2019) · Applied Surface Science · doi:10.1016/j.apsusc.2019.03.312 · Cited by 315 · see citing works

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Ji, X., Paulsen, B. D., Chik, G. K. K., Wu, R., Yin, Y., Chan, P. K. L., & Rivnay, J. (2021) · Nature Communications · doi:10.1038/s41467-021-22680-5 · Cited by 320 · see citing works

Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems

Shim, H., Sim, K., Ershad, F., Yang, P., Thukral, A., Rao, Z., Kim, H., Liu, Y., Wang, X., Gu, G., et al. (2019) · Science Advances · doi:10.1126/sciadv.aax4961 · Cited by 314 · see citing works

Light-Stimulated Synaptic Devices Utilizing Interfacial Effect of Organic Field-Effect Transistors

Dai, S., Wu, X., Liu, D., Chu, Y., Wang, K., Yang, B., & Huang, J. D. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b05036 · Cited by 308 · see citing works

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

Song, M., Kang, J., Zhang, X., Ji, W., Ascoli, A., Messaris, I., Demirkol, A. Ş., Dong, B., Aggarwal, S., Wan, W., et al. (2023) · ACS Nano · doi:10.1021/acsnano.3c03505 · Cited by 314 · see citing works

Oxide-based RRAM materials for neuromorphic computing

Hong, X., Loy, D. J., Dananjaya, P. A., Tan, F., Ng, C., & Lew, W. S. (2018) · Journal of Materials Science · doi:10.1007/s10853-018-2134-6 · Cited by 307 · see citing works

Nanoarchitectonics: a new materials horizon for nanotechnology

Ariga, K., Ji, Q., Nakanishi, W., Hill, J. P., & Aono, M. (2015) · Materials Horizons · doi:10.1039/c5mh00012b · Cited by 305 · see citing works

Emerging Artificial Synaptic Devices for Neuromorphic Computing

Wan, Q., Sharbati, M. T., Erickson, J. R., Du, Y., & Xiong, F. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201900037 · Cited by 303 · see citing works

Nanoarchitectonics: what's coming next after nanotechnology?

Ariga, K. (2021) · Nanoscale Horizons · doi:10.1039/d0nh00680g · Cited by 301 · see citing works

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing

John, R. A., Demirağ, Y., Shynkarenko, Y., Berezovska, Y., Ohannessian, N., Payvand, M., Zeng, P., Bodnarchuk, M. I., Krumeich, F., Kara, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29727-1 · Cited by 298 · see citing works

Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing

Ling, H., Koutsouras, D. A., Kazemzadeh, S., van de Burgt, Y., Yan, F., & Gkoupidenis, P. (2020) · Applied Physics Reviews · doi:10.1063/1.5122249 · Cited by 296 · see citing works

Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks

Srikimkaew, O., Azhari, S., Banerjee, D., Usami, Y., & Tanaka, H. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202400360 · Cited by 11 · see citing works

Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks (Adv. Electron. Mater. 12/2024)

Srikimkaew, O., Azhari, S., Banerjee, D., Usami, Y., & Tanaka, H. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202470039

Engineering Complex Synaptic Behaviors in a Single Device: Emulating Consolidation of Short-term Memory to Long-term Memory in Artificial Synapses via Dielectric Band Engineering

Tao, J., Sarkar, D., Kale, S., Singh, P. K., & Kapadia, R. (2020) · Nano Letters · doi:10.1021/acs.nanolett.0c03548 · Cited by 48 · see citing works

Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems

Li, Y., Zhong, Y., Zhang, J., Xu, L., Wang, Q., Sun, H., Tong, H., Cheng, X., & Miao, X. (2014) · Scientific Reports · doi:10.1038/srep04906 · Cited by 344 · see citing works

Emulating dynamic synaptic plasticity over broad timescales with memristive device

Zhang, Y., Zhong, S., Song, L., Ji, X., & Zhao, R. J. (2018) · Applied Physics Letters · doi:10.1063/1.5052556 · Cited by 28 · see citing works