The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Experimental demonstration of associative memory with memristive neural networks
Abstract
Synapses are essential elements for computation and information storage in both real and artificial neural systems. An artificial synapse needs to remember its past dynamical history, store a continuous set of states, and be "plastic" according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive off-the-shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behavior with electronic neural networks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Previously suggested chaotic circuits 19-22 , involving those with memristors 22 , involve several circuit elements.”
From Bistable Nonvolatile Elastic-Membrane Memcapacitor Exhibiting a Chaotic Behavior (2011) · cites this paper as [19]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 1×
“for short [20], [21]) based on TiO 2 thin films [22], [23] offers a promising realization of a synapse whose size can be as small as 30×30×2 nm(3).”
BackgroundMemristive 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
Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex.
Pavlov, P. I. (1930) · Archives of Neurology And Psychiatry · doi:10.1001/archneurpsyc.1930.02220110256018 · Cited by 2,681
Memristive devices and systems
Chua, L. O., & Kang, S. M. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10092 · Cited by 2,604 · cited 3×
“,t is a generalized response (memresistance R , or memductance G ), x is a ndimensional vector describing the internal state of the device, and f ()x,u,t is a continuous n-dimensional vector function [7, 8]. As Fig. 2a illustrates schematically, our memristor emulator consists of the following units: a digital potenti…”
BackgroundInfluentialAnderson, J. R. (2013) · Psychology Press eBooks · doi:10.4324/9780203780954 · Cited by 2,180
Neural Networks: A Systematic Introduction
Rojas, R. (1996) · Medical Entomology and Zoology · Cited by 2,142
Conditioned Reflexes: an Investigation of the Physiological Activity of the Cerebral Cortex
Denny-Brown, D. E. (1928) · Nature · doi:10.1038/121662a0 · Cited by 1,854
Research works printed but not linked: 4
- Arthur, 2006, Vol. 18, 75
- Bofill, 2001, Vol. 14, 1091
- Snider, 2008, Cortical computing with memristive nanodevices, SciDAC Review, 10, 58
- Vogelstein, R., Malik, U., & Cauwenberghs, G. (2007). Silicon spikebased synaptic array and address-event transceiver. In Proceedings of ISCAS04: Vol. 5 (pp. 385–388)..
Cited by
Sorted by citations
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
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
Shafiee, A. R., Nag, A., Muralimanohar, N., Balasubramonian, R., Strachan, J. P., Hu, M., Williams, R. S., & Srikumar, V. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001139 · Cited by 1,554
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
Observation of conducting filament growth in nanoscale resistive memories
Yang, Y., Gao, P., Gaba, S., Chang, T., Pan, X., & Lu, W. (2012) · Nature Communications · doi:10.1038/ncomms1737 · Cited by 1,208
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 1,196
A scalable neuristor built with Mott memristors
Pickett, M. D., Medeiros‐Ribeiro, G., & Williams, R. S. (2012) · Nature Materials · doi:10.1038/nmat3510 · Cited by 1,046
Memory effects in complex materials and nanoscale systems
Pershin, Y. V., & Di Ventra, M. (2011) · Advances In Physics · doi:10.1080/00018732.2010.544961 · Cited by 823
Self-citationISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
Shafiee, A., Nag, A., Muralimanohar, N., Balasubramonian, R., Strachan, J. P., Hu, M., Williams, R. S., & Srikumar, V. (2016) · 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca.2016.12 · Cited by 775
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
Practical Approach to Programmable Analog Circuits With Memristors
Pershin, Y. V., & Di Ventra, M. (2010) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2009.2038539 · Cited by 639
Self-citationSparse 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
IMPLEMENTING MEMRISTOR BASED CHAOTIC CIRCUITS
Muthuswamy, B. (2010) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127410026514 · Cited by 607
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
Zamarreño-Ramos, C., Camuñas-Mesa, L. A., Pérez‐Carrasco, J. A., Masquelier, T., Serrano‐Gotarredona, T., & Linares-Barranco, B. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00026 · Cited by 564
Chen, J., Zeng, Z., & Jiang, P. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.11.016 · Cited by 555
Memristor-based neural networks
Thomas, A. (2013) · Journal of Physics D: Applied Physics · doi:10.1088/0022-3727/46/9/093001 · Cited by 471
Memristor-based memory: The sneak paths problem and solutions
Zidan, M. A., Fahmy, H. A. H., Hussain, M. M., & Salama, K. N. (2012) · Microelectronics Journal · doi:10.1016/j.mejo.2012.10.001 · Cited by 452
Neural networks: An overview of early research, current frameworks and new challenges
Prieto, A., Prieto, B., Ortigosa, E. M., Ros, E., Pelayo, F. J., Ortega, J., & Rojas, I. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.014 · Cited by 411
Dynamical Properties and Design Analysis for Nonvolatile Memristor Memories
Ho, Y., Huang, G. M., & Li, P. (2010) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2010.2078710 · Cited by 326
Nonlinear Dynamics of Memristor Oscillators
Corinto, F., Ascoli, A., & Gilli, M. (2011) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2010.2097731 · Cited by 324
Adaptive oxide electronics: A review
Ha, S. D., & Ramanathan, S. (2011) · Journal of Applied Physics · doi:10.1063/1.3640806 · Cited by 308
Review on chaotic dynamics of memristive neuron and neural network
Lin, H., Wang, C., Deng, Q., Xu, C., Deng, Z., & Zhou, C. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-06853-x · Cited by 308
Exponential Stabilization of Memristive Neural Networks With Time Delays
Wu, A., & Zeng, Z. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2219554 · Cited by 295
Polymer memristor for information storage and neuromorphic applications
Chen, Y., Liu, G., Wang, C., Zhang, W., Li, R., & Wang, L. (2014) · Materials Horizons · doi:10.1039/c4mh00067f · Cited by 289
Capacitive neural network with neuro-transistors
Wang, Z., Rao, M., Han, J.-W., Zhang, J., Lin, P., Li, Y., Li, C., Song, W., Asapu, S., Midya, R., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05677-5 · Cited by 283
Ventra, M. D., & Pershin, Y. V. (2013) · Nature Physics · doi:10.1038/nphys2566 · Cited by 276
Self-citationMagneto-ionic control of magnetism using a solid-state proton pump
Tan, A. J., Huang, M., Avci, C. O., Büttner, F., Mann, M., Hu, W., Mazzoli, C., Wilkins, S. B., Tuller, H. L., & Beach, G. S. D. (2018) · Nature Materials · doi:10.1038/s41563-018-0211-5 · Cited by 280
Coupled oscillators for computing: A review and perspective
Csaba, G., & Porod, W. (2020) · Applied Physics Reviews · doi:10.1063/1.5120412 · Cited by 290
Arithmetic and Biologically‐Inspired Computing Using Phase‐Change Materials
Wright, C. D., Liu, Y., Kohary, K. I., Aziz, M. M., & Hicken, R. J. (2011) · Advanced Materials · doi:10.1002/adma.201101060 · Cited by 278
Nanoscale resistive switching devices: mechanisms and modeling
Yang, Y., & Lu, W. D. (2013) · Nanoscale · doi:10.1039/c3nr03472k · Cited by 271
Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements
Pershin, Y. V., & Ventra, M. D. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2166369 · Cited by 266
Self-citationAssociative memory realized by a reconfigurable memristive Hopfield neural network
Hu, S. G., Liu, Y., Liu, Z., Chen, T. P., Wang, J., Yu, Q., Deng, L. J., Yin, Y., & Hosaka, S. (2015) · Nature Communications · doi:10.1038/ncomms8522 · Cited by 260
A chaotic circuit based on Hewlett-Packard memristor
Buscarino, A., Fortuna, L., Frasca, M., & Gambuzza, L. V. (2012) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4729135 · Cited by 254
Global Mittag–Leffler Stabilization of Fractional-Order Memristive Neural Networks
Wu, A., & Zeng, Z. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2506738 · Cited by 254
Nonvolatile Memory Materials for Neuromorphic Intelligent Machines
Jeong, D. S., & Hwang, C. S. (2018) · Advanced Materials · doi:10.1002/adma.201704729 · Cited by 247
Exponential Synchronization of Memristive Neural Networks With Delays: Interval Matrix Method
Yang, X., Cao, J., & Liang, J. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2561298 · Cited by 241
Zhang, G., & Shen, Y. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.03.009 · Cited by 235
Sun, J., Han, G., Zeng, Z., & Wang, Y. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2951520 · Cited by 226
Bao, H., Park, J. H., & Cao, J. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2475737 · Cited by 227
Nanoionics‐Enabled Memristive Devices: Strategies and Materials for Neuromorphic Applications
Wang, Z., Wang, L., Nagai, M., Xie, L., Yi, M., & Huang, W. (2017) · Advanced Electronic Materials · doi:10.1002/aelm.201600510 · Cited by 224
Emergent Criticality in Complex Turing B‐Type Atomic Switch Networks
Stieg, A. Z., Avizienis, A. V., Sillin, H. O., Martin‐Olmos, C., Aono, M., & Gimzewski, J. K. (2011) · Advanced Materials · doi:10.1002/adma.201103053 · Cited by 212
Karam, R., Puri, R., Ghosh, S., & Bhunia, S. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2434888 · Cited by 217
A novel memristive neural network with hidden attractors and its circuitry implementation
Pham, V. T., Jafari, S., Vaidyanathan, S. V., VOLOS, C. K., & Wang, X. (2015) · Science China Technological Sciences · doi:10.1007/s11431-015-5981-2 · Cited by 214
Yang, S., Guo, Z., & Wang, J. (2015) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2014.2388199 · Cited by 207
An Electronic Version of Pavlov's Dog
Ziegler, M., Soni, R., Patelczyk, T., Ignatov, M., Bartsch, T., Meuffels, P., & Kohlstedt, H. (2012) · Advanced Functional Materials · doi:10.1002/adfm.201200244 · Cited by 211
Lagrange Stability of Memristive Neural Networks With Discrete and Distributed Delays
Wu, A., & Zeng, Z. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2280458 · Cited by 204
Finite-time synchronization for memristor-based neural networks with time-varying delays
Abdurahman, A., Jiang, H., & Teng, Z. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.04.015 · Cited by 202
Memristors: Devices, Models, and Applications [Scanning the Issue]
Mazumder, P., Kang, S. M., & Waser, R. M. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2190812 · Cited by 201
Driscoll, T., Quinn, J., Klein, S., Kim, H. T., Kim, B.-S., Pershin, Y. V., Ventra, M. D., & Basov, D. N. (2010) · Applied Physics Letters · doi:10.1063/1.3485060 · Cited by 198
Self-citationAdaptive Synchronization of Memristor-Based Neural Networks with Time-Varying Delays
Wang, L., Shen, Y., Yin, Q., & Zhang, G. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2361776 · Cited by 200
Hardware realization of BSB recall function using memristor crossbar arrays
Hu, M., Li, H. H., Wu, Q., & Rose, G. S. (2012) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2228360.2228448 · Cited by 191
Guo, Z., Wang, J., & Yan, Z. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.08.002 · Cited by 191
Memristive circuits simulate memcapacitors and meminductors
Pershin, Y. V., & Ventra, M. D. (2010) · Electronics Letters · doi:10.1049/el.2010.2830 · Cited by 191
Self-citationGlobal Synchronization of Fuzzy Memristive Neural Networks With Discrete and Distributed Delays
Wang, L., He, H., & Zeng, Z. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2019.2930032 · Cited by 191
Zhang, G., & Shen, Y. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2345125 · Cited by 189
Towards artificial neurons and synapses: a materials point of view
Jeong, D. S., Kim, I., Ziegler, M., & Kohlstedt, H. (2012) · RSC Advances · doi:10.1039/c2ra22507g · Cited by 191
Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array
Eryilmaz, S. B., Kuzum, D., Jeyasingh, R., Kim, S., BrightSky, M., Lam, C., & Wong, H.-S. P. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00205 · Cited by 205
Exponential stability analysis of memristor-based recurrent neural networks with time-varying delays
Wen, S., Zeng, Z., & Huang, T. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.06.014 · Cited by 184
Anti-synchronization control of a class of memristive recurrent neural networks
Wu, A., & Zeng, Z. (2012) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2012.07.005 · Cited by 181
Attractivity Analysis of Memristor-Based Cellular Neural Networks With Time-Varying Delays
Guo, Z., Wang, J., & Yan, Z. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2280556 · Cited by 181
Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays
Wu, A., & Zeng, Z. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.08.009 · Cited by 185
Zhang, G., Shen, Y., & Wang, L. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.04.001 · Cited by 179
A Memristive Multilayer Cellular Neural Network With Applications to Image Processing
Hu, X., Feng, G., Duan, S., & Liu, L. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2552640 · Cited by 173
Memristor: A New Concept in Synchronization of Coupled Neuromorphic C ircuits
VOLOS, C. K., Kyprianidis, I. M., Stouboulos, I. N., Cuautle, E. T. , & Vaidyanathan, S. V. (2015) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.082.21 · Cited by 171
Solving mazes with memristors: A massively parallel approach
Pershin, Y. V., & Ventra, M. D. (2011) · Physical Review E · doi:10.1103/physreve.84.046703 · Cited by 163
Self-citationExponential Stability of Complex-Valued Memristive Recurrent Neural Networks
Wang, H., Duan, S., Huang, T., Wang, L., & Li, C. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2513001 · Cited by 163
Chaotic memristive circuit: equivalent circuit realization and dynamical analysis
Bao, B., Xu, J., Zhou, G., Ma, Z., & Zou, L. (2011) · Chinese Physics B · doi:10.1088/1674-1056/20/12/120502 · Cited by 160
Cantley, K. D., Subramaniam, A., Stiegler, H. J., Chapman, R. A., & Vogel, E. M. (2011) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2011.2105887 · Cited by 164
Guo, Z., Yang, S., & Wang, J. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2354432 · Cited by 158
Memristor-based reservoir computing
Kulkarni, M. S., & Teuscher, C. (2012) · IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) · doi:10.1145/2765491.2765531 · Cited by 160
Liu, H., Wang, Z., Shen, B., & Liu, X. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2728639 · Cited by 156
Mimicking Classical Conditioning Based on a Single Flexible Memristor
Wu, C., Kim, T. W., Guo, T., Li, F., Lee, D. U., & Yang, J. J. (2016) · Advanced Materials · doi:10.1002/adma.201602890 · Cited by 151
Zhang, G., Zeng, Z., & Hu, J. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.10.003 · Cited by 145
Experimental photonic quantum memristor
Spagnolo, M., Morris, J., Piacentini, S., Antesberger, M., Massa, F., Crespi, A., Ceccarelli, F., Osellame, R., & Walther, P. J. (2022) · Nature Photonics · doi:10.1038/s41566-022-00973-5 · Cited by 141
Wang, L., Zeng, Z., & Ge, M. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2888867 · Cited by 141
Pham, V., Volos, C., Vaidyanathan, S., Le, T. P., & Vu, V. (2015) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.082.26 · Cited by 156
Global Anti-Synchronization of Complex-Valued Memristive Neural Networks With Time Delays
Liu, D., Zhu, S., & Sun, K. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2812708 · Cited by 139
Nanofluidic logic with mechano–ionic memristive switches
Emmerich, T., Teng, Y., Ronceray, N., Lopriore, E., Chiesa, R., Chernev, A. L., Artemov, V. G., Ventra, M. D., Kis, A., & Rađenović, A. (2024) · Nature Electronics · doi:10.1038/s41928-024-01137-9 · Cited by 143
Emulation of floating memcapacitors and meminductors using current conveyors
Pershin, Y. V., & Ventra, M. D. (2011) · Electronics Letters · doi:10.1049/el.2010.7328 · Cited by 136
Self-citationControlling Chaos in a Memristor Based Circuit Using a Twin-T Notch Filter
Iu, H. H., Yu, D., Fitch, A. L., Sreeram, V., & Chen, H. (2011) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2010.2097771 · Cited by 136
Self‐Limited Switching in Ta2O5/TaOx Memristors Exhibiting Uniform Multilevel Changes in Resistance
Kim, K. M., Lee, S. R., Kim, S., Chang, M., & Hwang, C. S. (2015) · Advanced Functional Materials · doi:10.1002/adfm.201403621 · Cited by 129
Non-fragile H∞ synchronization of memristor-based neural networks using passivity theory
Mathiyalagan, K., Anbuvithya, R., Sakthivel, R., Park, J. H., & Prakash, P. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.11.005 · Cited by 127
On hyperchaos in a small memristive neural network
Li, Q., Tang, S., Zeng, H., & Zhou, T. (2014) · Nonlinear Dynamics · doi:10.1007/s11071-014-1498-7 · Cited by 125
Fixed-time synchronization of quaternion-valued memristive neural networks with time delays
Wei, R., & Cao, J. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.014 · Cited by 125
Bifurcations in a fractional-order BAM neural network with four different delays
Huang, C., Wang, J., Chen, X., & Cao, J. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.04.005 · Cited by 125
Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks
Wang, L., Shen, Y., & Zhang, G. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2598598 · Cited by 124
Neuromorphic Computing with Memristor Crossbar
Zhang, X., Huang, A., Hu, Q., Xiao, Z., & Chu, P. K.-H. (2018) · physica status solidi (a) · doi:10.1002/pssa.201700875 · Cited by 126
Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors
Cantley, K. D., Subramaniam, A., Stiegler, H. J., Chapman, R. A., & Vogel, E. M. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2184801 · Cited by 121
Implementation of Memristive Neural Network With Full-Function Pavlov Associative Memory
Liu, X., Zeng, Z., & Wen, S. (2016) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2016.2570819 · Cited by 120
Kim, S., Chen, J., Chen, Y., Kim, M., Kim, H., Kwon, M.-W., Hwang, S., Ismail, M., Li, Y., Miao, X., et al. (2018) · Nanoscale · doi:10.1039/c8nr06694a · Cited by 119
Finite-Time Stability of Delayed Memristor-Based Fractional-Order Neural Networks
Chen, C., Zhu, S., Wei, Y., & Chen, C. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2876901 · Cited by 113
Romeira, B., Javaloyes, J., Ironside, C. N., Figueiredo, J. M. L., Balle, S., & Piro, O. (2013) · Optics Express · doi:10.1364/oe.21.020931 · Cited by 113
Chandrasekar, A., Rakkiyappan, R., Cao, J., & Lakshmanan, S. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.06.001 · Cited by 112
A novel memristive time–delay chaotic system without equilibrium points
Pham, V., Vaidyanathan, S., Volos, C., Jafari, S., Kuznetsov, N., & Hoang, T. (2016) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2016-02625-8 · Cited by 117
Stability and synchronization of fractional-order memristive neural networks with multiple delays
Chen, L., Cao, J., Wu, R., Machado, J. A. T., Lopes, A. M., & Yang, H. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.06.012 · Cited by 111
Low‐Conductance and Multilevel CMOS‐Integrated Nanoscale Oxide Memristors
Sheng, X., Graves, C. E., Kumar, S., Li, X., Buchanan, B., Zheng, L., Lam, S., Li, C., & Strachan, J. P. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800876 · Cited by 109
Solution-processed electronics for artificial synapses
Lu, K., Li, X., Sun, Q., Pang, X., Chen, J., Minari, T., Liu, X., & Song, Y. (2020) · Materials Horizons · doi:10.1039/d0mh01520b · Cited by 112
State estimation of fractional-order delayed memristive neural networks
Bao, H., Cao, J., & Kurths, J. (2018) · Nonlinear Dynamics · doi:10.1007/s11071-018-4419-3 · Cited by 109
Lagrange Stability for T–S Fuzzy Memristive Neural Networks with Time-Varying Delays on Time Scales
Xiao, Q., & Zeng, Z. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2017.2704059 · Cited by 108
Building Neuromorphic Circuits with Memristive Devices
Chang, T., Yang, Y., & Lu, W. D. (2013) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2013.2256260 · Cited by 108
Metadata
- Authors
- Pershin, Y. V., & Di Ventra, M.
- Affiliations
- University of South Carolina (US)
- Corresponding author
- Pershin, Yuriy V.
- ORCID
- Pershin, Yuriy V.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 23
- Issue
- 7
- Pages
- 881-886
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2015088745
- Cited by
- 1,041
- References
- 44
- Citations by year
- 2026: 26 · 2025: 38 · 2024: 51 · 2023: 53 · 2022: 68 · 2021: 73 · 2020: 84 · 2019: 85 · 2018: 99 · 2017: 61 · 2016: 76 · 2015: 64 · 2014: 93 · 2013: 66 · 2012: 56
- FWCI
- 42.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- memristors, associative memory, memristive neural networks, artificial synapses, synaptic plasticity, electronic neurons, neuromorphic hardware
- MeSH terms
- Artificial Intelligence, Association Learning, Models, Neurological, Nerve Net, Neuronal Plasticity, Neurons, Synapses, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
