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.
“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 · see citing works
Self-citationNEURAL EXCITABILITY, SPIKING AND BURSTING
Izhikevich, E. M. (2000) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127400000840 · Cited by 1,994 · see citing works
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 · see citing works
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 · see citing works
Weakly Connected Neural Networks
Hoppensteadt, F. C., & Izhikevich, E. M. (1997) · Applied mathematical sciences · doi:10.1007/978-1-4612-1828-9 · Cited by 1,264 · see citing works
Self-citationGray, C. M., & McCormick, D. A. (1996) · Science · doi:10.1126/science.274.5284.109 · Cited by 906 · see citing works
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 · see citing works
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 615 · see citing works
Self-citationIzhikevich, E. M. (2001) · Neural Networks · doi:10.1016/s0893-6080(01)00078-8 · Cited by 509 · see citing works
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 · see citing works
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Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Dynamical Systems in Neuroscience
Izhikevich, E. M. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/2526.001.0001 · Cited by 3,776 · see citing works
Self-citationWhich Model to Use for Cortical Spiking Neurons?
Izhikevich, E. M. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.832719 · Cited by 2,555 · see citing works
Self-citationTowards spike-based machine intelligence with neuromorphic computing
Roy, K., Jaiswal, A., & Panda, P. (2019) · Nature · doi:10.1038/s41586-019-1677-2 · Cited by 2,267 · see citing works
The future of electronics based on memristive systems
Zidan, M. A., Strachan, J. P., & Lu, W. D. (2018) · Nature Electronics · doi:10.1038/s41928-017-0006-8 · Cited by 2,138 · see citing works
Neuromorphic Silicon Neuron Circuits
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Deep learning in spiking neural networks
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., & Maida, A. S. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.12.002 · Cited by 1,420 · see citing works
Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity
Brette, R., & Gerstner, W. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00686.2005 · Cited by 1,285 · see citing works
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Sengupta, A., Ye, Y., Wang, R. Y., Liu, C., & Roy, K. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00095 · Cited by 1,214 · see citing works
Towards artificial general intelligence with hybrid Tianjic chip architecture
Pei, J., Deng, L., Song, S., Zhao, M., Zhang, Y., Wu, S., Wang, G., Zou, Z., Wu, Z., He, W., et al. (2019) · Nature · doi:10.1038/s41586-019-1424-8 · Cited by 1,082 · see citing works
Ghosh‐Dastidar, S., & Adeli, H. (2009) · International Journal of Neural Systems · doi:10.1142/s0129065709002002 · Cited by 1,066 · see citing works
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Izhikevich, E. M. (2005) · Neural Computation · doi:10.1162/089976606775093882 · Cited by 1,066 · see citing works
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Pickett, M. D., Medeiros‐Ribeiro, G., & Williams, R. S. (2012) · Nature Materials · doi:10.1038/nmat3510 · Cited by 1,040 · see citing works
Simulation of networks of spiking neurons: A review of tools and strategies
Brette, R., Rudolph, M., Carnevale, T., Hines, M. L., Beeman, D., Bower, J. M., Diesmann, M., Morrison, A., Goodman, P. H., Harris, F. C., et al. (2007) · Journal of Computational Neuroscience · doi:10.1007/s10827-007-0038-6 · Cited by 827 · see citing works
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Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F., Sumislawska, D., & Indiveri, G. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00141 · Cited by 678 · see citing works
A Survey of Neuromorphic Computing and Neural Networks in HardwarePreprint
Schuman, C. D., Potok, T. E., Patton, R. M., Birdwell, J. D., Dean, M. E., Rose, G. S., & Plank, J. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.06963 · Cited by 861 · see citing works
Artificial neural networks in hardware: A survey of two decades of progress
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Spiking Neural Networks and Their Applications: A Review
Yamazaki, K., Vo-Ho, V.-K., Bulsara, D., & Le, N. (2022) · Brain Sciences · doi:10.3390/brainsci12070863 · Cited by 653 · see citing works
PyNN: a common interface for neuronal network simulators
Davison, A. P. (2008) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.011.2008 · Cited by 622 · see citing works
A neuronal model of predictive coding accounting for the mismatch negativity.
Wacongne, C., Changeux, J.-P., & Dehaene, S. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5003-11.2012 · Cited by 542 · see citing works
Reato, D., Rahman, A., Bikson, M., & Parra, L. C. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2059-10.2010 · Cited by 576 · see citing works
Ponulak, F., & Kasiński, A. (2009) · Neural Computation · doi:10.1162/neco.2009.11-08-901 · Cited by 575 · see citing works
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SpiNNaker: A 1-W 18-Core System-on-Chip for Massively-Parallel Neural Network Simulation
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Chimera states in neuronal networks: A review
Majhi, S., Bera, B. K., Ghosh, D., & Perc, M. (2018) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.09.003 · Cited by 488 · see citing works
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Self-citationAli, M. M., Sellers, K. K., & Frӧhlich, F. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.5867-12.2013 · Cited by 476 · see citing works
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SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
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Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain
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A review of learning in biologically plausible spiking neural networks
Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L., & McGinnity, T. M. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.036 · Cited by 424 · see citing works
Finding a roadmap to achieve large neuromorphic hardware systems
Hasler, J., & Marr, B. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00118 · Cited by 389 · see citing works
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 396 · see citing works
Self-organization and neuronal avalanches in networks of dissociated cortical neurons
Pasquale, V., Massobrio, P., Bologna, L. L., Chiappalone, M., & Sergio Martinoia, A. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.03.050 · Cited by 384 · see citing works
Frenkel, C., Lefebvre, M., Legat, J.-D., & Bol, D. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2880425 · Cited by 390 · 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
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
Rethinking the performance comparison between SNNS and ANNS
Deng, L., Wu, Y., Hu, X., Liang, L., Ding, Y., Li, G., Zhao, G., Li, P., & Xie, Y. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.005 · Cited by 345 · see citing works
Oddo, C. M., Raspopović, S., Artoni, F., Mazzoni, A., Spigler, G., Petrini, F. M., Giambattistelli, F., Vecchio, F., Miraglia, F., Zollo, L., et al. (2016) · eLife · doi:10.7554/elife.09148 · Cited by 328 · see citing works
Map-based models in neuronal dynamics
Ibarz, B., Casado, J. M., & Sanjuán, M. A. F. (2010) · Physics Reports · doi:10.1016/j.physrep.2010.12.003 · Cited by 322 · see citing works
Firing patterns in the adaptive exponential integrate-and-fire model
Naud, R., Marcille, N., Clopath, C., & Wulfram Gerstner, B. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0264-7 · Cited by 326 · see citing works
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Ashwin, P., Coombes, S., & Nicks, R. (2016) · The Journal of Mathematical Neuroscience · doi:10.1186/s13408-015-0033-6 · Cited by 288 · see citing works
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
Hazan, H., Saunders, D. J., Khan, H., Patel, D., Sanghavi, D. T., Siegelmann, H. T., & Kozma, R. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00089 · Cited by 287 · see citing works
SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor
Khan, M. M., Lester, D., Plana, L. A., Rast, A. D., Jin, X., Painkras, E., & Furber, S. B. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634199 · Cited by 282 · see citing works
Computing with Spiking Neuron Networks
Paugam‐Moisy, H., & Bohté, S. M. (2012) · Springer eBooks · doi:10.1007/978-3-540-92910-9_10 · Cited by 283 · see citing works
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Teeter, C., Iyer, R., Menon, V., Gouwens, N. W., Feng, D. H., Berg, J., Szafer, A., Cain, N., Zeng, H., Hawrylycz, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02717-4 · Cited by 277 · see citing works
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Hwang, C. S. (2015) · Advanced Electronic Materials · doi:10.1002/aelm.201400056 · Cited by 248 · see citing works
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Pfister, J.-P., Toyoizumi, T., Barber, D., & Gerstner, W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.6.1318 · Cited by 246 · see citing works
Computational Maps in the Visual Cortex
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Volatile and Nonvolatile Memristive Devices for Neuromorphic Computing
Zhou, G., Wang, Z., Sun, B. T., Zhou, F., Sun, L., Zhao, H., Hu, X., Peng, X., Yan, J., Wang, H., et al. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202101127 · Cited by 245 · see citing works
A Tutorial for Information Theory in Neuroscience
Timme, N. M., & Lapish, C. C. (2018) · eNeuro · doi:10.1523/eneuro.0052-18.2018 · Cited by 241 · see citing works
Causal connectivity of evolved neural networks during behavior
Seth, A. K. (2005) · Network Computation in Neural Systems · doi:10.1080/09548980500238756 · Cited by 242 · see citing works
Review of pulse-coupled neural networks
Wang, Z., Ma, Y., Cheng, F., & Yang, L. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.06.007 · Cited by 242 · see citing works
Nonvolatile Memory Materials for Neuromorphic Intelligent Machines
Jeong, D. S., & Hwang, C. S. (2018) · Advanced Materials · doi:10.1002/adma.201704729 · Cited by 244 · see citing works
Ito, S., Hansen, M. E., Heiland, R. W., Lumsdaine, A., Litke, A. M., & Beggs, J. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027431 · Cited by 234 · see citing works
Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET
Dutta, S., Kumar, V., Shukla, A., Mohapatra, N. R., & Ganguly, U. (2017) · Scientific Reports · doi:10.1038/s41598-017-07418-y · Cited by 226 · see citing works
An Evolvable Organic Electrochemical Transistor for Neuromorphic Applications
Gerasimov, J. Y., Gabrielsson, R., Forchheimer, R., Stavrinidou, E., Simon, D. T., Magnus Berggren, A., & Fabiano, S. (2019) · Advanced Science · doi:10.1002/advs.201801339 · Cited by 223 · see citing works
Capra, M., Bussolino, B., Marchisio, A., Masera, G., Martina, M., & Shafique, M. (2020) · IEEE Access · doi:10.1109/access.2020.3039858 · Cited by 228 · see citing works
Embodied neuromorphic intelligence
Bartolozzi, C., Indiveri, G., & Donati, E. (2022) · Nature Communications · doi:10.1038/s41467-022-28487-2 · Cited by 231 · see citing works
Spiking Neural Networks: A Survey
Nunes, J. D., Carvalho, M., Carneiro, D., & Cardoso, J. S. (2022) · IEEE Access · doi:10.1109/access.2022.3179968 · Cited by 223 · see citing works
Stable concurrent synchronization in dynamic system networks
Pham, Q., & Slotine, J.-J. E. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.07.008 · Cited by 206 · see citing works
Zhao, B., Ding, R., Chen, S., Linares-Barranco, B., & Tang, H. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2362542 · Cited by 215 · see citing works
Conditional modulation of spike-timing-dependent plasticity for olfactory learning
Cassenaer, S., & Laurent, G. (2012) · Nature · doi:10.1038/nature10776 · Cited by 215 · see citing works
Robust Flexible Pressure Sensors Made from Conductive Micropyramids for Manipulation Tasks
Ma, C., Xu, D., Huang, Y.-C., Wang, P., Huang, J. S., Zhou, J., Liu, W., Li, S., Huang, Y., & Duan, X. (2020) · ACS Nano · doi:10.1021/acsnano.0c03659 · Cited by 215 · see citing works
Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience
McDougal, R. A., Morse, T. M., Carnevale, T., Marenco, L. N., Wang, R., Migliore, M., Miller, P. L., Shepherd, G. M., & Hines, M. L. (2016) · Journal of Computational Neuroscience · doi:10.1007/s10827-016-0623-7 · Cited by 214 · see citing works
Predicting spike timing of neocortical pyramidal neurons by simple threshold models
Jolivet, R., Rauch, A., Lüscher, H., & Wulfram Gerstner, B. (2006) · Journal of Computational Neuroscience · doi:10.1007/s10827-006-7074-5 · Cited by 211 · see citing works
Neural-like computing with populations of superparamagnetic basis functions
Mizrahi, F. A., Hirtzlin, T., Fukushima, A., Kubota, H., Yuasa, S., Grollier, J., & Querlioz, D. (2018) · Nature Communications · doi:10.1038/s41467-018-03963-w · Cited by 204 · see citing works
Yu, Q., Tang, H., Tan, K. C., & Li, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078318 · Cited by 202 · see citing works
Seth, A. K., Chorley, P., & Barnett, L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.049 · Cited by 194 · see citing works
Bidirectional plasticity in fast-spiking GABA circuits by visual experience
Yazaki-Sugiyama, Y., Kang, S., Câteau, H., Fukai, T., & Hensch, T. K. (2009) · Nature · doi:10.1038/nature08485 · Cited by 200 · see citing works
Decoupling through Synchrony in Neuronal Circuits with Propagation Delays
Lubenov, E. V., & Siapas, A. G. (2008) · Neuron · doi:10.1016/j.neuron.2008.01.036 · Cited by 196 · see citing works
Transcranial Current Brain Stimulation (tCS): Models and Technologies
Ruffini, G., Wendling, F., Merlet, I., Molaee-Ardekani, B., Mekonnen, A., Salvador, R., Soria‐Frisch, A., Grau, C., Dunne, S., & Miranda, P. C. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2200046 · Cited by 196 · see citing works
Guo, D., Wang, Q., & Perc, M. (2012) · Physical Review E · doi:10.1103/physreve.85.061905 · Cited by 197 · see citing works
Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments
Ardin, P., Peng, F., Mangan, M., Lagogiannis, K., & Webb, B. H. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004683 · Cited by 197 · see citing works
Paredes-Vallés, F., Scheper, K. Y. W., & de Croon, G. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2903179 · Cited by 197 · see citing works
Fluctuations in Oscillation Frequency Control Spike Timing and Coordinate Neural Networks
Cohen, M. X. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0261-14.2014 · Cited by 195 · see citing works
Artificial Neuron and Synapse Devices Based on 2D Materials
Lee, G., Baek, J., Ren, F., Pearton, S. J., Lee, G., & Kim, J. (2021) · Small · doi:10.1002/smll.202100640 · Cited by 194 · see citing works
Seo, D., Lee, Y., Go, G., Pei, M., Jung, S., Jeong, Y. H., Lee, W., Park, H., Kim, S., Yang, H., et al. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.104035 · Cited by 189 · see citing works
Temporal whitening by power-law adaptation in neocortical neurons
Pozzorini, C., Naud, R., Mensi, S., & Gerstner, W. (2013) · Nature Neuroscience · doi:10.1038/nn.3431 · Cited by 183 · see citing works
Nageswaran, J. M., Dutt, N. D., Krichmar, J. L., Nicolau, A., & Veidenbaum, A. V. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.06.028 · Cited by 186 · see citing works
Oscillations in Large-Scale Cortical Networks: Map-Based Model
Rulkov, N. F., Timofeev, I., & Bazhenov, M. (2004) · Journal of Computational Neuroscience · doi:10.1023/b:jcns.0000037683.55688.7e · Cited by 185 · see citing works
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
Zhang, M., Wang, J., Wu, J., Belatreche, A., Amornpaisannon, B., Zhang, Z., Miriyala, V. P. K., Qu, H., Chua, Y., Carlson, T. E., et al. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3110991 · Cited by 203 · see citing works
Garofalo, M., Nieus, T. R., Massobrio, P., & Sergio Martinoia, A. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006482 · Cited by 184 · see citing works
Noise focusing and the emergence of coherent activity in neuronal cultures
Orlandi, J. G., Soriano, J., Álvarez-Lacalle, E., Teller, S., & Casademunt, J. (2013) · Nature Physics · doi:10.1038/nphys2686 · Cited by 184 · see citing works
Macroscopic resting-state brain dynamics are best described by linear models
Nozari, E., Bertolero, M. A., Stiso, J., Caciagli, L., Cornblath, E. J., He, X., Mahadevan, A. S., Pappas, G. J., & Bassett, D. S. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01117-y · Cited by 178 · see citing works
A robot model of the basal ganglia: Behavior and intrinsic processing
Prescott, T. J., Montes-González, F., GURNEY, K. N., Humphries, M. D., & Redgrave, P. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.049 · Cited by 182 · see citing works
Challenges for large-scale implementations of spiking neural networks on FPGAs
Maguire, L., McGinnity, T. M., Glackin, B., Ghani, A., Belatreche, A., & Harkin, J. (2007) · Neurocomputing · doi:10.1016/j.neucom.2006.11.029 · Cited by 181 · see citing works
- 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
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