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Simple model of spiking neurons
Abstract
A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
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“The neuron state transition influences downstream neural activity. Using classical neural models like Hodgkin-Huxley or integrate-and-fire (Izhikevich, 2003), the firing probability of a neuron i could be described as (see Equation 5):”
From The collapse of the wave function as the mediator of free will in prime neurons (2025) · cites this paper as Izhikevich, 2003
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Dynamical Systems in Neuroscience
Izhikevich, E. M. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/2526.001.0001 · Cited by 3,776
Self-citationNEURAL EXCITABILITY, SPIKING AND BURSTING
Izhikevich, E. M. (2000) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127400000840 · Cited by 1,994
Self-citationIntrinsic firing patterns of diverse neocortical neurons
Connors, B. W., & Gutnick, M. J. (1990) · Trends in Neurosciences · doi:10.1016/0166-2236(90)90185-d · Cited by 1,440
Two networks of electrically coupled inhibitory neurons in neocortex
Gibson, J. R., Beierlein, M., & Connors, B. W. (1999) · Nature · doi:10.1038/47035 · Cited by 1,404
Weakly Connected Neural Networks
Hoppensteadt, F. C., & Izhikevich, E. M. (1997) · Applied mathematical sciences · doi:10.1007/978-1-4612-1828-9 · Cited by 1,261
Self-citationGray, C. M., & McCormick, D. A. (1996) · Science · doi:10.1126/science.274.5284.109 · Cited by 906
Parabolic Bursting in an Excitable System Coupled with a Slow Oscillation
Ermentrout, G. B., & Kopell, N. (1986) · SIAM Journal on Applied Mathematics · doi:10.1137/0146017 · Cited by 627
Bursts as a unit of neural information: selective communication via resonance
Izhikevich, E. M., Desai, N. S., Walcott, E. C., & Hoppensteadt, F. C. (2003) · Trends in Neurosciences · doi:10.1016/s0166-2236(03)00034-1 · Cited by 606
Self-citationIzhikevich, E. M. (2001) · Neural Networks · doi:10.1016/s0893-6080(01)00078-8 · Cited by 509
Self-citationGlobal and partial synchronism in phase-locked loop networks
Monteiro, L. H. A., Canto, N. C. F., Chauí‐Berlinck, J. G., Orsatti, F. M., & Piqueira, J. R. C. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820441 · Cited by 24
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Shastri, B. J., Nahmias, M. A., Tait, A. N., Rodríguez, A. W., Wu, B., & Prucnal, P. R. (2016) · Scientific Reports · doi:10.1038/srep19126 · Cited by 179
Rich-Club Organization in Effective Connectivity among Cortical Neurons
Nigam, S., Shimono, M., Ito, S., Yeh, F.-C., Timme, N. M., Myroshnychenko, M., Lapish, C. C., Tosi, Z., Hottowy, P., Smith, W. C., et al. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2177-15.2016 · Cited by 176
GeNN: a code generation framework for accelerated brain simulations
Yavuz, E., Turner, J. P., & Nowotny, T. (2016) · Scientific Reports · doi:10.1038/srep18854 · Cited by 171
NetPyNE, a tool for data-driven multiscale modeling of brain circuits
Dura-Bernal, S., Suter, B. A., Gleeson, P., Cantarelli, M., Quintana, A., Rodriguez, F., Kedziora, D. J., Chadderdon, G. L., Kerr, C. C., Neymotin, S. A., et al. (2019) · eLife · doi:10.7554/elife.44494 · Cited by 169
Technological advancements and opportunities in Neuromarketing: a systematic review
Rawnaque, F. S., Rahman, K. M., Anwar, S. F., Vaidyanathan, R., Chau, T. T., Sarker, F., & Mamun, K. A. (2020) · Brain Informatics · doi:10.1186/s40708-020-00109-x · Cited by 167
Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences
He, W., Wu, Y., Deng, L., Li, G., Wang, H., Tian, Y., Ding, W., Wang, W., & Xie, Y. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.08.001 · Cited by 167
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Yan, X., Qian, J. H., Sangwan, V. K., & Hersam, M. C. (2021) · Advanced Materials · doi:10.1002/adma.202108025 · Cited by 172
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Toker, D., Sommer, F. T., & D’Esposito, M. (2020) · Communications Biology · doi:10.1038/s42003-019-0715-9 · Cited by 172
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Hu, Y., Deng, L., Wu, Y., Yao, M., & Li, G. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3355393 · Cited by 227
Fan, L. Z., Kheifets, S., Böhm, U. L., Wu, H., Piatkevich, K. D., Xie, M. E., Parot, V., Ha, Y., Evans, K. E., Boyden, E. S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.001 · Cited by 160
Izhikevich, E. M. (2010) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2010.0130 · Cited by 159
Self-citationA Generalized Linear Integrate-and-Fire Neural Model Produces Diverse Spiking Behaviors
Mihalaş, Ş., & Niebur, E. (2008) · Neural Computation · doi:10.1162/neco.2008.12-07-680 · Cited by 159
Laminar Organization of Attentional Modulation in Macaque Visual Area V4
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Ding, S., Wang, N., Bao, H., Chen, B., Wu, H., & Xu, Q. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112899 · Cited by 155
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Soleimani, H., Ahmadi, A., & Bavandpour, M. (2012) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2012.2206463 · Cited by 154
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Lowet, E., Roberts, M. J., Bonizzi, P., Karel, J. M. H., & Weerd, P. D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0146443 · Cited by 150
Li, S., Zhang, Z., Mao, R., Xiao, J., Chang, L., & Zhou, J. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3052885 · Cited by 158
libRoadRunner: a high performance SBML simulation and analysis library
Somogyi, E., Bouteiller, J. C., Glazier, J. A., König, M., Medley, K., Swat, M., & Sauro, H. M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv363 · Cited by 152
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Yu, Q., Tang, H., Tan, K. C., & Li, H. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2245677 · Cited by 150
A world survey of artificial brain projects, Part I: Large-scale brain simulations
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Xiang, S., Zhang, Y., Gong, J., Guo, X., Lin, L., & Hao, Y. (2019) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2019.2911565 · Cited by 148
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Mechanisms of Zero-Lag Synchronization in Cortical Motifs
Gollo, L. L., Mirasso, C. R., Sporns, O., & Breakspear, M. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003548 · Cited by 155
Autaptic regulation of electrical activities in neuron under electromagnetic induction
Xu, Y., Ying, H.-P., Jia, Y., Ma, J., & Hayat, T. (2017) · Scientific Reports · doi:10.1038/srep43452 · Cited by 146
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Heidarpur, M., Ahmadi, A., Ahmadi, M., & Azghadi, M. R. (2019) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2019.2899356 · Cited by 144
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Shrestha, A., Fang, H., Mei, Z., Rider, D. P., Wu, Q., & Qiu, Q. (2022) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2022.3166331 · Cited by 152
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A benchmark test for a quantitative assessment of simple neuron models
Jolivet, R., Kobayashi, R., Rauch, A., Naud, R., Shinomoto, S., & Wulfram Gerstner, B. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.11.006 · Cited by 145
Tetzlaff, C. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00047 · Cited by 142
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Wade, J. J., Mcdaid, L. J., Harkin, J., Crunelli, V., & Kelso, J. A. S. (2011) · PLoS ONE · doi:10.1371/journal.pone.0029445 · Cited by 143
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Song, T., Rodríguez‐Patón, A., Zheng, P., & Zeng, X. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2785332 · Cited by 135
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Farazmand, M., & Sapsis, T. P. (2018) · Applied Mechanics Reviews · doi:10.1115/1.4042065 · Cited by 148
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Wang, C.-S., Lin, P.-J., Cheng, C., Tai, S.-H., Yang, Y. K., & Chiang, J.-H. (2018) · Journal of Medical Internet Research · doi:10.2196/11016 · Cited by 136
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Network inference with confidence from multivariate time series
Kramer, M., Eden, U. T., Cash, S. S., & Kolaczyk, E. D. (2009) · Physical Review E · doi:10.1103/physreve.79.061916 · Cited by 128
GURNEY, K. N., Humphries, M. D., & Redgrave, P. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002034 · Cited by 127
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Frӧhlich, F. (2015) · Progress in brain research · doi:10.1016/bs.pbr.2015.07.025 · Cited by 136
Shahidi, F., Daud, S. M., Abas, H., Ahmad, N. A., & Maarop, N. (2020) · IEEE Access · doi:10.1109/access.2020.3029881 · Cited by 136
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Kim, H., Won, Y., Song, H. W., Kwon, Y., Jun, M., & Oh, J. H. (2023) · Advanced Science · doi:10.1002/advs.202306191 · Cited by 130
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di Volo, M., Romagnoni, A., Capone, C., & Alain Destexhe, A. (2019) · Neural Computation · doi:10.1162/neco_a_01173 · Cited by 128
Kafraj, M. S., Parastesh, F., & Jafari, S. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109782 · Cited by 119
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Kumaravelu, K., Brocker, D. T., & Grill, W. M. (2016) · Journal of Computational Neuroscience · doi:10.1007/s10827-016-0593-9 · Cited by 111
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Raman, B., Joseph, J., Tang, J., & Stopfer, M. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5639-09.2010 · Cited by 110
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Sankar, S., Wenyu, C., Zhang, J., Slepyan, A., Iskarous, M. M., Greene, R. J., DeBrabander, R., Chen, J., Gupta, A., & Thakor, N. V. (2025) · Science Advances · doi:10.1126/sciadv.adr9300 · Cited by 119
Benda, J., Maler, L., & Longtin, A. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00240.2010 · Cited by 109
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Zilli, E. A., & Hasselmo, M. E. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0547-10.2010 · Cited by 108
Lobov, S. A., Mikhaylov, A. N., Shamshin, M., Makarov, V. A., & Kazantsev, V. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00088 · Cited by 108
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Learning rules in spiking neural networks: A survey
Yi, Z., Lian, J., Liu, Q., Zhu, H., Liang, D., & Liu, J. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.026 · Cited by 112
Gong, P., Wang, P., Zhou, Y., & Zhang, D. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3246989 · Cited by 113
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Guo, D., Wu, S., Chen, M., Perc, M., Zhang, Y., Ma, J., Cui, Y., Xu, P., Xia, Y., & Yao, D. (2016) · Scientific Reports · doi:10.1038/srep26096 · Cited by 109
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Guo, D., & Li, C. (2009) · Physical Review E · doi:10.1103/physreve.79.051921 · Cited by 105
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Fidjeland, A. K., & Shanahan, M. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596678 · Cited by 106
Frenkel, C., Bol, D., & Indiveri, G. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3273520 · Cited by 160
Zeng, Y., Zhao, D., Zhao, F., Shen, G., Dong, Y., Lu, E., Zhang, Q., Sun, Y., Liang, Q., Zhao, Y., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100789 · Cited by 119
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Adeli, H., & Ghosh‐Dastidar, S. (2010) · Thammasat University Digital Collections · Cited by 103
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Zhang, S., Li, C., Zheng, J., Wang, X., Zeng, Z., & Chen, G. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3225847 · Cited by 103
Resonant Interneurons Can Increase Robustness of Gamma Oscillations
Tikidji-Hamburyan, R. A., Martínez, J. J., White, J. A., & Canavier, C. C. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2601-15.2015 · Cited by 98
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NeMo: A Platform for Neural Modelling of Spiking Neurons Using GPUs
Fidjeland, A. K., Roesch, E. B., Shanahan, M., & Luk, W. W. (2009) · IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP) · doi:10.1109/asap.2009.24 · Cited by 101
Moore, S. W., Fox, P. J., Marsh, S. J. T., Markettos, A. T., & Mujumdar, A. (2012) · IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) · doi:10.1109/fccm.2012.32 · Cited by 100
Taherkhani, A., Belatreche, A., Li, Y., & Maguire, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2797801 · Cited by 99
Chou, T.-S., Kashyap, H. J., Xing, J., Listopad, S., Rounds, E. L., Beyeler, M., Dutt, N. D., & Krichmar, J. L. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489326 · Cited by 99
Metadata
- Authors
- Izhikevich, E. M.
- Affiliations
- Neurosciences Institute (US)
- Corresponding author
- Izhikevich, E.M.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2003-11
- Volume
- 14
- Issue
- 6
- Pages
- 1569-1572
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2164653071
- Cited by
- 4,828
- References
- 10
- Citations by year
- 2026: 263 · 2025: 412 · 2024: 364 · 2023: 393 · 2022: 376 · 2021: 317 · 2020: 280 · 2019: 267 · 2018: 274 · 2017: 250 · 2016: 192 · 2015: 248 · 2014: 211 · 2013: 186 · 2012: 180
- FWCI
- 13.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Keywords
- spiking neuron models, cortical neurons, bursting behavior, integrate-and-fire neurons, computational efficiency
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 ›
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