The DOI matches its Crossref record; 139 of the 141 references it cites were found in a registry or an index.
Memristor-based neural networks
Open accessAbstract
The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them.
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?
The cited source says what the claim attributes to it.
In the citing paper
“The modern memristor provides us with just such an adaptive pathway.”
From AHaH Computing–From Metastable Switches to Attractors to Machine Learning (2014) · cites this paper as [83]
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
Hodgkin, A. L., & Huxley, A. F. (1952) · The Journal of Physiology · doi:10.1113/jphysiol.1952.sp004764 · Cited by 23,191
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
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
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 2×
“If we follow Chua’s original line of argumentation in his seminal paper in which he introduced the memristor, we find the following concept [1].”
BackgroundInfluentialHebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Bliss, T. V. P., & Gardner-Medwin, A. R. (1973) · The Journal of Physiology · doi:10.1113/jphysiol.1973.sp010274 · Cited by 7,424
indicated, N. A. (1950) · Journal of Consulting Psychology · doi:10.1037/h0049612 · Cited by 6,984
The Organization of Behavior; A Neuropsychological Theory.
Attneave, F., B., M., & Hebb, D. O. (1950) · The American Journal of Psychology · doi:10.2307/1418888 · Cited by 5,146
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
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 · cited 1×
“Jo et al proposed another neuromorphic system that utilizes CMOS neurons and nanoscale memristive synapses [86].”
BackgroundPossible new effects in superconductive tunnelling
Josephson, B. D. (1962) · Physics Letters · doi:10.1016/0031-9163(62)91369-0 · Cited by 4,143
Prinz, G. A. (1998) · Science · doi:10.1126/science.282.5394.1660 · Cited by 3,613
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 2×
“whereVM and I indicate the voltage across and current through the device, respectively, and R denotes the memristance [4].”
BackgroundNeuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
Koch, C. (1998) · Oxford University Press eBooks · doi:10.1093/oso/9780195104912.001.0001 · Cited by 2,306
Lee, M.-J., Lee, C. B., Lee, D., Lee, S. R., Chang, M., Hur, J. H., Kim, Y.-B., Kim, C.-J., Seo, D. H., Seo, S., et al. (2011) · Nature Materials · doi:10.1038/nmat3070 · Cited by 2,228
Reliability of spike timing in neocortical neurons
Mainen, Z. F., & Sejnowski, T. J. (1995) · Science · doi:10.1126/science.7770778 · Cited by 1,950
‘Memristive’ switches enable ‘stateful’ logic operations via material implication
Borghetti, J., Snider, G. S., Kuekes, P. J., Yang, J. J., Stewart, D. R., & Williams, R. S. (2010) · Nature · doi:10.1038/nature08940 · Cited by 1,929
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
Electronically Configurable Molecular-Based Logic Gates
Collier, C. P., Wong, E. W., Bělohradský, M., Raymo, F. M., Stoddart, J. F., Kuekes, P. J., Williams, R. S., & Heath, J. R. (1999) · Science · doi:10.1126/science.285.5426.391 · Cited by 1,519
Current-Induced Switching of Domains in Magnetic Multilayer Devices
Myers, E. B., Ralph, D. C., Katine, J. A., Louie, R. N., & Buhrman, R. A. (1999) · Science · doi:10.1126/science.285.5429.867 · Cited by 1,379
Electric current flow in excitable cells
(1976) · American Heart Journal · doi:10.1016/s0002-8703(76)80560-1 · Cited by 1,289
Complementary resistive switches for passive nanocrossbar memories
Linn, E., Rosezin, R., Kügeler, C., & Waser, R. M. (2010) · Nature Materials · doi:10.1038/nmat2748 · Cited by 1,286
Chua, L. O. (1987) · Cited by 1,245
Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors
Di Ventra, M., Pershin, Y. V., & Chua, L. O. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2021077 · Cited by 1,212
Spin-polarized electron tunneling
Meservey, R., & Tedrow, P. M. (1994) · Physics Reports · doi:10.1016/0370-1573(94)90105-8 · Cited by 1,175
Introduction to Theoretical Neurobiology
Tuckwell, H. C. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511623202 · Cited by 1,174
Cole, K. S. (1968) · University of California Press eBooks · doi:10.1525/9780520326514 · Cited by 1,165
Pattern recognition computation using action potential timing for stimulus representation
Hopfield, J. J. (1995) · Nature · doi:10.1038/376033a0 · Cited by 1,154
SPICE Model of Memristor with Nonlinear Dopant Drift
Biolek, Z., Biolek, D., & Biolková, V. (2009) · Brno University of Technology Digital Library (Brno University of Technology) · Cited by 1,100
Short-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor
Chang, T., Jo, S.-H., & Lu, W. (2011) · ACS Nano · doi:10.1021/nn202983n · Cited by 1,058
Experimental demonstration of associative memory with memristive neural networks
Pershin, Y. V., & Di Ventra, M. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.05.001 · Cited by 1,041
Maze—solving by an amoeboid organism
Nakagaki, T., Yamada, H., & Tóth, Á. (2000) · Nature · doi:10.1038/35035159 · Cited by 1,022
Indiveri, G., Chicca, E., & Douglas, R. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.860850 · Cited by 1,009
Gray, C. M., & McCormick, D. A. (1996) · Science · doi:10.1126/science.274.5284.109 · Cited by 911
The mechanism of electroforming of metal oxide memristive switches
Yang, J. J., Miao, F., Pickett, M. D., Ohlberg, D. A. A., Stewart, D. R., Lau, C. N., & Williams, R. S. (2009) · Nanotechnology · doi:10.1088/0957-4484/20/21/215201 · Cited by 862
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 851
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 · cited 1×
“The result is a characteristic, pinched hysteresis curve. an even further generalization of equation (1), please refer to Pershin and Di Ventra [5].”
BackgroundDynamics of Encoding in a Population of Neurons
Knight, B. W. (1972) · The Journal of General Physiology · doi:10.1085/jgp.59.6.734 · Cited by 720
Reproducibility and Variability in Neural Spike Trains
van Steveninck, R. R. D. R., Lewen, G. D., Strong, S. P., Köberle, R., & Bialek, W. (1997) · Science · doi:10.1126/science.275.5307.1805 · Cited by 686
Conditioned Reflexes and Neuron Organization.
(1949) · Annals of Internal Medicine · doi:10.7326/0003-4819-31-6-1133_1 · Cited by 685
Memristor−CMOS Hybrid Integrated Circuits for Reconfigurable Logic
Xia, Q., Robinett, W., Cumbie, M. W., Banerjee, N., Cardinali, T. J., Yang, J. J., Wu, W., Li, X., Tong, W. M., Strukov, D. B., et al. (2009) · Nano Letters · doi:10.1021/nl901874j · Cited by 671
An overview of sigma-delta converters
Aziz, P. M., Sorensen, H. V., & der Spiegel, J. V. (1996) · IEEE Signal Processing Magazine · doi:10.1109/79.482138 · Cited by 664
CNN: A Paradigm for Complexity
Chua, L. O. (1998) · World scientific series on nonlinear science, series A · doi:10.1142/9789812798589 · Cited by 650
Synaptic enhancement in fascia dentata: Cooperativity among coactive afferents
McNaughton, B. L., Douglas, R. M., & Goddard, G. V. (1978) · Brain Research · doi:10.1016/0006-8993(78)90030-6 · Cited by 647
High switching endurance in TaOx memristive devices
Yang, J. J., Zhang, M.-X., Strachan, J. P., Miao, F., Pickett, M. D., Kelley, R. D., Medeiros-Ribeiro, G., & Williams, R. S. (2010) · Applied Physics Letters · doi:10.1063/1.3524521 · Cited by 637
Histology Of The Nervous System
Cajal, S. R. Y., Azoulay, D. L., swanson, N., & Swanson, L. W. (1995) · doi:10.1093/oso/9780195074017.001.0001 · Cited by 634
Heterosynaptic depression: a postsynaptic correlate of long-term potentiation
LYNCH, G. S., DUNWIDDIE, T., & GRIBKOFF, V. (1977) · Nature · doi:10.1038/266737a0 · Cited by 615
Huai, Y., Albert, F., Nguyen, P. P., Pakala, M., & Valet, T. (2004) · Applied Physics Letters · doi:10.1063/1.1707228 · Cited by 582
Spin polarized tunneling in ferromagnetic junctions
Moodera, J. S., & Mathon, G. (1999) · Journal of Magnetism and Magnetic Materials · doi:10.1016/s0304-8853(99)00515-6 · Cited by 572
Synapses as associative memory elements in the hippocampal formation
Levy, W. B., & Steward, O. (1979) · Brain Research · doi:10.1016/0006-8993(79)91003-5 · Cited by 557
Histology of the nervous system of man and vertebrates
y Cajal, S. R. (1995) · Cited by 553
Experience-dependent, asymmetric expansion of hippocampal place fields
Mehta, M. R., Barnes, C. A., & McNaughton, B. L. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.16.8918 · Cited by 536
Programmable nanowire circuits for nanoprocessors
Yan, H., Choe, H. S., Nam, S., Hu, Y., Das, S., Klemic, J. F., Ellenbogen, J. C., & Lieber, C. M. (2011) · Nature · doi:10.1038/nature09749 · Cited by 524
Memristive model of amoeba learning
Pershin, Y. V., Fontaine, S. L., & Ventra, M. D. (2009) · Physical Review E · doi:10.1103/physreve.80.021926 · Cited by 489
Specific long-lasting potentiation of synaptic transmission in hippocampal slices
Andersen, P. N., Sundberg, S. H., Sveen, O., & Wigström, H. (1977) · Nature · doi:10.1038/266736a0 · Cited by 489
Synaptic behaviors and modeling of a metal oxide memristive device
Chang, T., Jo, S.-H., Kim, K.-H., Sheridan, P., Gaba, S., & Lu, W. (2011) · Applied Physics A · doi:10.1007/s00339-011-6296-1 · Cited by 432 · cited 1×
“A comparable experiment was conducted by Chang et al [87].”
MethodNeural network models for pattern recognition and associative memory
Carpenter, G. A. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90035-x · Cited by 419
Novelty acquisition is associated with induction of hippocampal long-term depression
Manahan‐Vaughan, D., & Braunewell, K. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.15.8739 · Cited by 358
Neural Synaptic Weighting With a Pulse-Based Memristor Circuit
Kim, H., Sah, M. P., Yang, C., Roska, T., & Chua, L. O. (2011) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2011.2161360 · Cited by 350
Spike-timing-dependent learning in memristive nanodevices
Snider, G. S. (2008) · 2008 IEEE International Symposium on Nanoscale Architectures · doi:10.1109/nanoarch.2008.4585796 · Cited by 341 · cited 2×
“In 2008, the use of memristors to mimic biological mechanisms, such as STDP, was already hypothesized [76].”
BackgroundHebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts
Cassenaer, S., & Laurent, G. (2007) · Nature · doi:10.1038/nature05973 · Cited by 335
Postsynaptic factors control the duration of synaptic enhancement in area CA1 of the hippocampus
Malenka, R. C. (1991) · Neuron · doi:10.1016/0896-6273(91)90121-f · Cited by 322
HODGKIN–HUXLEY AXON IS MADE OF MEMRISTORS
CHUA, L., SBITNEV, V., & KIM, H. (2012) · International Journal of Bifurcation and Chaos · doi:10.1142/s021812741230011x · Cited by 319
Hippocampal long-term depression is required for the consolidation of spatial memory
Ge, Y., Dong, Z., Bagot, R. C., Howland, J. G., Phillips, A. G., Wong, T. P., & Wang, Y. T. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008200107 · Cited by 298
MEMRISTOR CELLULAR AUTOMATA AND MEMRISTOR DISCRETE-TIME CELLULAR NEURAL NETWORKS
Itoh, M., & Chua, L. O. (2009) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127409025031 · Cited by 298
Dry electrodes for electrocardiography
Meziane, N., Webster, J. G., Attari, M., & Nimunkar, A. J. (2013) · Physiological Measurement · doi:10.1088/0967-3334/34/9/r47 · Cited by 289
Introduction to nonlinear network theory
Kuo, F. F. (1970) · Journal of the Franklin Institute · doi:10.1016/0016-0032(70)90177-8 · Cited by 284
Navigating Complex Labyrinths: Optimal Paths from Chemical Waves
Steinbock, O., Tóth, Á., & Showalter, K. (1995) · Science · doi:10.1126/science.267.5199.868 · Cited by 278
Associative long-term potentiation in hippocampal slices.
Barrionuevo, G., & Brown, T. H. (1983) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.80.23.7347 · Cited by 273
A hybrid nanomemristor/transistor logic circuit capable of self-programming
Borghetti, J., Li, Z., Straznicky, J., Li, X., Ohlberg, D. A. A., Wu, W., Stewart, D. R., & Williams, R. S. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806642106 · Cited by 270
Hippocampal synaptic plasticity and NMDA receptors: a role in information storage?
Morris, R., Davis, S., & Butcher, S. P. (1990) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.1990.0164 · Cited by 267
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-organized computation with unreliable, memristive nanodevices
Snider, G. S. (2007) · Nanotechnology · doi:10.1088/0957-4484/18/36/365202 · Cited by 264
A Memristor SPICE Implementation and a New Approach for Magnetic Flux-Controlled Memristor Modeling
Batas, D., & Fiedler, H. L. (2010) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2009.2038051 · Cited by 264
Electric Current Flow in Excitable Cells.
Cattaneo, A. (1984) · Bioelectrochemistry and Bioenergetics · doi:10.1016/0302-4598(84)87024-5 · Cited by 262
Spatial exploration induces a persistent reversal of long-term potentiation in rat hippocampus
Xu, L., Anwyl, R., & Rowan, M. J. (1998) · Nature · doi:10.1038/29783 · Cited by 260
Macromodeling of the Memristor in SPICE
Rák, Á., & Cserey, G. (2010) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2010.2042900 · Cited by 253
Blind source identification from the multichannel surface electromyogram
Holobar, A., & Farina, D. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/7/r143 · Cited by 238
Memristor Cellular Automata and Memristor Discrete-Time Cellular Neural Networks
Itoh, M., & Chua, L. O. (2013) · Springer eBooks · doi:10.1007/978-3-319-02630-5_30 · Cited by 231
Limits on the memory storage capacity of bounded synapses
Fusi, S., & Abbott, L. F. (2007) · Nature Neuroscience · doi:10.1038/nn1859 · Cited by 226
The fourth element: characteristics, modelling and electromagnetic theory of the memristor
Kavehei, O., Iqbal, A., Kim, Y. S., Eshraghian, K., Al-Sarawi, S. F., & Abbott, D. (2010) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2009.0553 · Cited by 224
Compact Models for Memristors Based on Charge-Flux Constitutive Relationships
Shin, S., Kim, K., & Kang, S.-M. S. (2010) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2010.2042891 · Cited by 219
Current-Driven Magnetization Switching in CoFeB/MgO/CoFeB Magnetic Tunnel Junctions
Hayakawa, J., Ikeda, S., Lee, Y. M., Sasaki, R., Meguro, T., Matsukura, F., Takahashi, H., & Ohno, H. (2005) · Japanese Journal of Applied Physics · doi:10.1143/jjap.44.l1267 · Cited by 215
A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity
Rachmuth, G., Shouval, H. Z., Bear, M. F., & Poon, C. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1106161108 · Cited by 214
The Memristive Magnetic Tunnel Junction as a Nanoscopic Synapse‐Neuron System
Krzysteczko, P., Münchenberger, J., Schäfers, M., Reiss, G., & Thomas, A. (2012) · Advanced Materials · doi:10.1002/adma.201103723 · Cited by 211
Self-citationIntroduction to nonlinear network theory
Chua, L. O. (1969) · Medical Entomology and Zoology · Cited by 194
CMOS-like logic in defective, nanoscale crossbars
Snider, G. S., Kuekes, P. J., & Williams, R. S. (2004) · Nanotechnology · doi:10.1088/0957-4484/15/8/003 · Cited by 192
Psychological stress repeatedly blocks hippocampal primed burst potentiation in behaving rats
Diamond, D. M., Fleshner, M., & Rose, G. M. (1994) · Behavioural Brain Research · doi:10.1016/0166-4328(94)90032-9 · Cited by 182
RECTIFICATION AND INDUCTANCE IN THE SQUID GIANT AXON
Cole, K. S. (1941) · The Journal of General Physiology · doi:10.1085/jgp.25.1.29 · Cited by 167
Deuchars, J., West, D. C., & Thomson, A. M. (1994) · The Journal of Physiology · doi:10.1113/jphysiol.1994.sp020262 · Cited by 165
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
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
The time courses of excitatory and inhibitory synaptic actions
Curtis, D., & Eccles, J. C. (1959) · The Journal of Physiology · doi:10.1113/jphysiol.1959.sp006159 · Cited by 154
The frequency of nerve action potentials generated by applied currents
Stein, R. B., & Hodgkin, A. L. (1967) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1967.0013 · Cited by 152
Distributed ARTMAP: a neural network for fast distributed supervised learning
Carpenter, G. A., Milenova, B. L., & Noeske, B. W. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00019-7 · Cited by 151
Neurons are Poised Near the Edge of Chaos
Chua, L., Sbitnev, V. I., & Kim, H. (2012) · International Journal of Bifurcation and Chaos in Applied Sciences and Engineering · doi:10.1142/s0218127412500988 · Cited by 148
Optimization of an organic memristor as an adaptive memory element
Berzina, T., Smerieri, A., Bernabò, M., Pucci, A., Ruggeri, G., Erokhin, V. V., & Fontana, M. P. (2009) · Journal of Applied Physics · doi:10.1063/1.3153944 · Cited by 139
Computing with hysteretic resistor crossbars
Snider, G. (2005) · Applied Physics A · doi:10.1007/s00339-004-3149-1 · Cited by 137
The molecular foundations of psychiatry
(1993) · Choice Reviews Online · doi:10.5860/choice.30-4099 · Cited by 129
LONGITUDINAL IMPEDANCE OF THE SQUID GIANT AXON
Cole, K. S., & Baker, R. F. (1941) · The Journal of General Physiology · doi:10.1085/jgp.24.6.771 · Cited by 125
Resistive switching behavior of Pt/Mg0.2Zn0.8O/Pt devices for nonvolatile memory applications
Chen, X., Wu, G., & Bao, D. (2008) · Applied Physics Letters · doi:10.1063/1.2978158 · Cited by 124
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
Neuromorphic architectures for nanoelectronic circuits
Türel, Ö., Lee, J. H., Ma, X., & Likharev, K. K. (2004) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.282 · Cited by 116
CrossNets: Neuromorphic Hybrid CMOS/Nanoelectronic Networks
Likharev, K. K. (2011) · Science of Advanced Materials · doi:10.1166/sam.2011.1177 · Cited by 115
Learning Navigational Maps Through Potentiation and Modulation of Hippocampal Place Cells
Wulfram Gerstner, B., & Abbott, L. F. (1997) · Journal of Computational Neuroscience · doi:10.1023/a:1008820728122 · Cited by 109
Doyère, V., & Laroche, S. (1992) · Hippocampus · doi:10.1002/hipo.450020106 · Cited by 101
From Synapses to Circuitry: Using Memristive Memory to Explore the Electronic Brain
Snider, G. S., Amerson, R., Carter, D., Abdalla, H., Qureshi, M. S., Léveillé, J., Massimiliano Versace, A., Ames, H., Patrick, S., Chandler, B., et al. (2011) · Computer · doi:10.1109/mc.2011.48 · Cited by 95
Anomalous Impedance, A Phenomenological Property of Time-Variant Resistance
Mauro, A. (1961) · Biophysical Journal · doi:10.1016/s0006-3495(61)86894-x · Cited by 90
Putting Memory Into Circuit Elements: Memristors, Memcapacitors, and Meminductors [Point of View]
Ventra, M. D., Pershin, Y. V., & Chua, L. O. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2022882 · Cited by 89
Cole, K. S. (2023) · University of California Press eBooks · doi:10.2307/jj.8501406 · Cited by 84
The structure of nerve fibres in cephalopods and crustacea
Young, J. Z. (1936) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1936.0069 · Cited by 81
Memristive switching of MgO based magnetic tunnel junctions
Krzysteczko, P., Reiss, G., & Thomas, A. (2009) · Applied Physics Letters · doi:10.1063/1.3224193 · Cited by 77
Self-citationMemristive switching of MgO based magnetic tunnel junctionsPreprint
Krzysteczko, P., Reiss, G., & Thomas, A. (2009) · Cited by 77
Self-citationLong-Term Depression Properties in a Simple System
Goda, Y., & Stevens, C. F. (1996) · Neuron · doi:10.1016/s0896-6273(00)80027-6 · Cited by 74
Sun, J., Gaidis, M. C., Hu, G., O'Sullivan, E. J., Brown, S. L., Nowak, J. J., Trouilloud, P. L., & Worledge, D. C. (2009) · Journal of Applied Physics · doi:10.1063/1.3058614 · Cited by 73
Min, T., Sun, J., Beach, R. S., Tang, D. D., & Wang, P. (2009) · Journal of Applied Physics · doi:10.1063/1.3063672 · Cited by 66
Деспотули, А. Л., & Николайчик, В. И. (1993) · Solid State Ionics · doi:10.1016/0167-2738(93)90005-n · Cited by 64
Erratum: Memristive model of amoeba learning [Phys. Rev. E80, 021926 (2009)]
Pershin, Y. V., Fontaine, S. L., & Ventra, M. D. (2010) · Physical Review E · doi:10.1103/physreve.82.019904 · Cited by 60
STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks
Afifi, A., Ayatollahi, A., & Raissi, F. (2009) · IEICE Electronics Express · doi:10.1587/elex.6.148 · Cited by 53
Daumas, S., Ceccom, J., Halley, H., Francès, B., & Lassalle, J. (2009) · Learning & Memory · doi:10.1101/lm.1418309 · Cited by 50
Plasticity of Mammalian Monosynaptic Reflexes
Eccles, J. C., & McIntyre, A. K. (1951) · Nature · doi:10.1038/167466a0 · Cited by 48
Functional Model of a Nanoparticle Organic Memory Transistor for Use as a Spiking Synapse
Bichler, O., Zhao, W. S., Alibart, F., Pleutin, S., Vuillaume, D., & Gamrat, C. (2010) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2010.2065951 · Cited by 44
Sigma-delta modulation with i.i.d. Gaussian inputs
Wong, P. W., & Gray, R. M. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.53738 · Cited by 38
Instar and outstar learning with memristive nanodevices
Snider, G. S. (2010) · Nanotechnology · doi:10.1088/0957-4484/22/1/015201 · Cited by 35
Motions of spiral waves in oscillatory media and in the presence of obstacles
Sepulchre, J.-A., & BABLOYANTZ, A. (1993) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.48.187 · Cited by 34
Current induced resistance change of magnetic tunnel junctions with ultra-thin MgO tunnel barriers
Krzysteczko, P., Kou, X., Rott, K., Thomas, A., & Reiss, G. (2008) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2008.08.088 · Cited by 28
Self-citationOrganic Memristor and Bio-Inspired Information Processing
Erokhin, V. V., Schüz, A., & Fontana, M. P. (2010) · Max Planck Institute for Plasma Physics · Cited by 21
Teaching Memory Circuit Elements via Experiment-Based Learning
Pershin, Y. V., & Ventra, M. D. (2012) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2011.2181096 · Cited by 20
1Teaching Memory Circuit Elements via Experiment-Based LearningPreprint
Pershin, Y. V., & Ventra, M. D. (2016) · Cited by 20
Low resistance magnetic tunnel junctions with MgO wedge barrier
Wrona, J., Langer, J., Ocker, B., Maaß, W., Kanak, J., Stobiecki, T., & Powroźnik, W. (2010) · Journal of Physics Conference Series · doi:10.1088/1742-6596/200/5/052032 · Cited by 13
The Molecular Foundations of Psychiatry
(1992) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9798894550176 · Cited by 13
Nobel Lectures in Physiology or Medicine 1922 – 1941
(1999) · WORLD SCIENTIFIC eBooks · doi:10.1142/3735 · Cited by 12
Münchenberger, J., Krzysteczko, P., Reiss, G., & Thomas, A. (2011) · Journal of Applied Physics · doi:10.1063/1.3660521 · Cited by 7
Self-citationPath Finding with Nonlinear Waves
Sepulchre, J.-A., BABLOYANTZ, A., & Steels, L. (1991) · Elsevier eBooks · doi:10.1016/b978-0-444-89178-5.50067-1 · Cited by 6
The memristive magnetic tunnel junction as a nanoscopic synapse-neuron system
Thomas, A., Krzysteczko, P., unchenberger, J. M., Reiß, G., & afers, M. S. (2012) · Bulletin of the American Physical Society · Cited by 2
Self-citationWilliams, D. G. (1995) · Science · doi:10.1126/science.269.5222.418 · Cited by 1
Research works printed but not linked: 9
- Snider G S, 2007, Nanotechnology, 18
- Yang J J, 2009, Nanotechnology, 20
- Biolek Z, 2009, Radioengineering, 18, 210
- Bliss T V P, 1973, J. Physiol., 232, 331, 10.1113/jphysiol.1973.sp010273 · DOI · Paper page
- Lapicque L, 1907, J. Physiol. Pathol. Gen., 9, 620
- Lapicque L, 1926, L'excitabilit en fonction du temps
- Erokhin V, 2010, Int. J. Unconv. Comput., 6, 15
- Steels L Babloyantz Sepulchre J A, 1991, Proc. Int. Conf. on Artificial Neural Networks
- Lindsten J, 1998, Nobel Lectures in Physiology or Medicine 1971–80
Cited by
Sorted by citations
Recent advances in physical reservoir computing: A review
Tanaka, G., Yamane, T., Héroux, J. B., Nakane, R., Kanazawa, N., Takeda, S., Numata, H., Nakano, D., & Hirose, A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.03.005 · Cited by 2,146
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
Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide
Sangwan, V. K., Lee, H., Bergeron, H., Balla, I., Beck, M. E., Chen, K., & Hersam, M. C. (2018) · Nature · doi:10.1038/nature25747 · Cited by 1,025
Yan, X., Zhao, Q., Chen, A. P., Zhao, J., Zhou, Z., Wang, J., Wang, H., Zhang, L., Li, X., Xiao, Z., et al. (2019) · Small · doi:10.1002/smll.201901423 · Cited by 421
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
Metadata
- Authors
- Thomas, A.
- Affiliations
- Universität Hamburg (DE)
- Corresponding author
- Thomas, Andy
- ORCID
- Thomas, Andy
- Venue
- Journal of Physics D: Applied Physics
- Publisher
- IOP Publishing
- Published
- 2013-02-05
- Volume
- 46
- Issue
- 9
- Pages
- 093001
- ISSN
- 0022-3727 (ISSN-L)
- 1361-6463
- OpenAlex
- W2159543793
- Cited by
- 471
- References
- 141
- Citations by year
- 2026: 10 · 2025: 41 · 2024: 50 · 2023: 51 · 2022: 40 · 2021: 43 · 2020: 32 · 2019: 37 · 2018: 42 · 2017: 31 · 2016: 31 · 2015: 26 · 2014: 22 · 2013: 13
- FWCI
- 18.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Physics D Applied Physics (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- memristors, artificial neural networks, synaptic plasticity, long-term potentiation, spike-timing-dependent plasticity, neuromorphic hardware, electronic synapses, biological neural networks, electric circuit model
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
