Parallel network simulations with NEURON
Open accessAbstract
The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2,000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.
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
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 previously built dentate gyrus model was used by several groups to study disparate topics including epilepsy, network mechanisms of inhibition and excitability, simulation optimization, and modeling software.
“Previously, we built a dentate gyrus model to study epileptic network dynamics (Santhakumar et al., 2005; Morgan and Soltesz, 2008) that was then used by several groups to study disparate topics including epilepsy, network mechanisms of inhibition and excitability, simulation optimization, and modeling software”
From Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit (2016) · cites this paper as Migliore et al., 2006
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The NEURON Simulation Environment
Hines, M. L., & Carnevale, N. T. (1997) · Neural Computation · doi:10.1162/neco.1997.9.6.1179 · Cited by 2,630 · see citing works
Self-citationMultilevelk-way Partitioning Scheme for Irregular Graphs
Karypis, G., & Kumar, V. (1998) · Journal of Parallel and Distributed Computing · doi:10.1006/jpdc.1997.1404 · Cited by 1,693 · see citing works
Carriero, N. J., & Gelernter, D. H. (1989) · Communications of the ACM · doi:10.1145/63334.63337 · Cited by 1,405 · see citing works
Markram, H. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1848 · Cited by 1,191 · see citing works
Self-citationMigliore, M., Hoffman, D. A., Magee, J. C., & Johnston, D. (1999) · Journal of Computational Neuroscience · doi:10.1023/a:1008906225285 · Cited by 334 · see citing works
Self-citationSanthakumar, V., Aradi, I., & Soltesz, I. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00777.2004 · Cited by 262 · see citing works
Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing
Morrison, A., Mehring, C., Geisel, T., Aertsen, A., & Diesmann, M. (2005) · Neural Computation · doi:10.1162/0899766054026648 · Cited by 201 · see citing works
Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses
Mattia, M., & Giudice, P. D. (2000) · Neural Computation · doi:10.1162/089976600300014953 · Cited by 167 · see citing works
Optimization of MPI collective communication on BlueGene/L systems
Almasi, G. S., Heidelberger, P., Archer, C. J., Martorell, X., Erway, C. C., Moreira, J. E., Steinmacher-Burow, B., & Zheng, Y. (2005) · International Conference on Supercomputing · doi:10.1145/1088149.1088183 · Cited by 160 · see citing works
SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons
Delorme, A., & Thorpe, S. J. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x/14/4/301 · Cited by 98 · see citing works
Dendrodendritic Inhibition and Simulated Odor Responses in a Detailed Olfactory Bulb Network Model
Davison, A. P., Feng, J., & Brown, D. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00623.2002 · Cited by 80 · see citing works
Bush, P. C., Prince, D. A., & Miller, K. D. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.82.4.1748 · Cited by 69 · see citing works
Large Neural Network Simulations on Multiple Hardware Platforms
Hammarlund, P., & Ekeberg, Ö. (1998) · Journal of Computational Neuroscience · doi:10.1023/a:1008893429695 · Cited by 58 · see citing works
SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons
Delorme, A., & Thorpe, S. J. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x_14_4_301 · Cited by 51 · see citing works
Independent Variable Time-Step Integration of Individual Neurons for Network Simulations
Lytton, W. W., & Hines, M. L. (2005) · Neural Computation · doi:10.1162/0899766053429453 · Cited by 43 · see citing works
Self-citationDiscrete event simulation in the NEURON environment
Hines, M. L., & Carnevale, N. T. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.01.175 · Cited by 39 · see citing works
Self-citationA large-scale model of the cerebellar cortex using PGENESIS
Howell, F. W., Dyhrfjeld‐Johnsen, J., Maex, R., Goddard, N., & Schutter, E. D. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00277-0 · Cited by 34 · see citing works
NEOSIM: Portable large-scale plug and play modelling
Goddard, N., Hood, G., Howell, F. W., Hines, M. L., & Schutter, E. D. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00528-8 · Cited by 31 · see citing works
Self-citationImplementation of a Biologically Realistic Parallel Neocortical-Neural Network Simulator.
Wilson, E., Goodman, P. H., & Harris, J. S. (2001) · Cited by 26 · see citing works
Large Neural Network Simulations on Multiple Hardware Platforms
Hammarlund, P., Ekeberg, Ö., Wilhelmsson, T., & Lansner, A. (1997) · Springer eBooks · doi:10.1007/978-1-4757-9800-5_142 · Cited by 14 · see citing works
Large-Scale Simulation Using Parallel GENESIS
Goddard, N., & Hood, G. (1998) · Springer eBooks · doi:10.1007/978-1-4612-1634-6_21 · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Reconstruction and Simulation of Neocortical Microcircuitry
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.029 · Cited by 1,705 · see citing works
Self-citationSimulation 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
Self-citationHines, M. L. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.001.2009 · Cited by 396 · see citing works
Self-citationNeural 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
Computer modelling of epilepsy
Lytton, W. W. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2416 · Cited by 286 · see citing works
Self-citationBezaire, M. J., Raikov, I., Burk, K., Vyas, D., & Soltesz, I. (2016) · eLife · doi:10.7554/elife.18566 · Cited by 198 · see citing works
neuroConstruct: A Tool for Modeling Networks of Neurons in 3D Space
Gleeson, P., Steuber, V., & Silver, R. A. (2007) · Neuron · doi:10.1016/j.neuron.2007.03.025 · Cited by 204 · see citing works
Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing
Morrison, A., Mehring, C., Geisel, T., Aertsen, A., & Diesmann, M. (2005) · Neural Computation · doi:10.1162/0899766054026648 · Cited by 201 · see citing works
Structural neurobiology: missing link to a mechanistic understanding of neural computation
Denk, W. T., Briggman, K. L., & Helmstaedter, M. N. (2012) · Nature reviews. Neuroscience · doi:10.1038/nrn3169 · Cited by 192 · see citing works
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 · see citing works
Self-citationThe Emergent neural modeling system
Aisa, B., Mingus, B., & O'Reilly, R. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.06.016 · Cited by 148 · see citing works
Why Are Computational Neuroscience and Systems Biology So Separate?
Schutter, E. D. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000078 · Cited by 108 · see citing works
CoreNEURON : An Optimized Compute Engine for the NEURON Simulator
Kumbhar, P., Hines, M. L., FOURIAUX, J., Ovcharenko, A., King, J. G., Delalondre, F., & Schürmann, F. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00063 · Cited by 90 · see citing works
Self-citationLinaro, D., Storace, M., & Giugliano, M. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001102 · Cited by 88 · see citing works
Akar, N. A., Cumming, B., Karakasis, V., Kusters, A., Klijn, W., Peyser, A., & Yates, S. (2019) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/empdp.2019.8671560 · Cited by 87 · see citing works
Pleßer, H. E., Eppler, J. M., Morrison, A., Diesmann, M., & Gewaltig, M.-O. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74466-5_71 · Cited by 87 · see citing works
Fully implicit parallel simulation of single neurons
Hines, M. L., Markram, H., & Schürmann, F. (2008) · Journal of Computational Neuroscience · doi:10.1007/s10827-008-0087-5 · Cited by 79 · see citing works
Self-citationReproducibility in Computational Neuroscience Models and Simulations
McDougal, R., Bulanova, A. S., & Lytton, W. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2539602 · Cited by 64 · see citing works
Self-citationNeurofitter: A parameter tuning package for a wide range of electrophysiological neuron models
Geit, W. A. H. V. (2007) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.001.2007 · Cited by 66 · see citing works
Translating network models to parallel hardware in NEURON
Hines, M. L., & Carnevale, N. T. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.09.010 · Cited by 66 · see citing works
Self-citationRealizing biological spiking network models in a configurable wafer-scale hardware system
Fieres, J., Schemmel, J., & Meier, K. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633916 · Cited by 66 · see citing works
Hines, M. L., & Carnevale, T. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_795 · Cited by 66 · see citing works
Self-citationCircuit reconstruction tools today
Smith, S. J. (2007) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2007.11.004 · Cited by 63 · see citing works
Hanuschkin, A., Kunkel, S., Helias, M., Morrison, A., & Diesmann, M. (2010) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2010.00113 · Cited by 61 · see citing works
Toward a full-scale computational model of the rat dentate gyrus
Schneider, C. J., Bezaire, M. J., & Soltész, I. (2012) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00083 · Cited by 60 · see citing works
Tsai, D., Chen, S. C., Protti, D. A., Morley, J. W., Suaning, G. J., & Lovell, N. H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0053357 · Cited by 56 · see citing works
BioNet: A Python interface to NEURON for modeling large-scale networks
Gratiy, S. L., Billeh, Y. N., Dai, K., Mitelut, C., Feng, D. H., Gouwens, N. W., Cain, N., Koch, C., Anastassiou, C. A., & Arkhipov, A. S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201630 · Cited by 53 · see citing works
HRLSim: A High Performance Spiking Neural Network Simulator for GPGPU Clusters
Minkovich, K., Thibeault, C. M., O'Brien, M. J., Nogin, A., Cho, Y., & Srinivasa, N. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2276056 · Cited by 54 · see citing works
How Big Data and High-Performance Computing Drive Brain Science
Chen, S., He, Z., Han, X., He, X., Li, R., Zhu, H., Zhao, D., Dai, C., Zhang, Y., Lu, Z., et al. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.09.003 · Cited by 54 · see citing works
A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling
Hoang, R. V., Tanna, D., Bray, L. C. J., Dascalu, S. M., & Harris, F. C. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00019 · Cited by 53 · see citing works
Software for Brain Network Simulations: A Comparative Study
Tikidji-Hamburyan, R. A., Narayana, V. K., Bozkuş, Z., & El-Ghazawi, T. A. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00046 · Cited by 53 · see citing works
Modernizing the NEURON Simulator for Sustainability, Portability, and Performance
Awile, O., Kumbhar, P., Cornu, N., Durá-Bernal, S., King, J. G., Lupton, O., Magkanaris, I., McDougal, R. A., Newton, A. J. H., Pereira, F., et al. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.884046 · Cited by 53 · see citing works
Self-citationAn Ultrascalable Solution to Large-scale Neural Tissue Simulation
Kozloski, J., & Wagner, J. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00015 · Cited by 50 · see citing works
A MODEL OF GRID CELLS INVOLVING EXTRA HIPPOCAMPAL PATH INTEGRATION, AND THE HIPPOCAMPAL LOOP
Gaussier, P., Banquet, J. P., Sargolini, F., GIOVANNANGELI, C., Save, É., & Poucet, B. (2007) · Journal of Integrative Neuroscience · doi:10.1142/s021963520700160x · Cited by 49 · see citing works
A GENERALIZED LEAKY INTEGRATE-AND-FIRE NEURON MODEL WITH FAST IMPLEMENTATION METHOD
Wang, Z., Guo, L., & Adjouadi, M. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065714400048 · Cited by 47 · see citing works
Hines, M. L., Eichner, H., & Schürmann, F. (2008) · Journal of Computational Neuroscience · doi:10.1007/s10827-007-0073-3 · Cited by 47 · see citing works
Self-citationMeeting the Memory Challenges of Brain-Scale Network Simulation
Kunkel, S., Potjans, T. C., Eppler, J. M., Pleßer, H. E., Morrison, A., & Diesmann, M. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00035 · Cited by 45 · see citing works
Experimentally guided modelling of dendritic excitability in rat neocortical pyramidal neurones
Keren, N., Bar‐Yehuda, D., & Korngreen, A. (2009) · The Journal of Physiology · doi:10.1113/jphysiol.2008.167130 · Cited by 45 · see citing works
Propagation of Seizure-like Activity in a Model of Neocortex
van Drongelen, W., Lee, H. C., Stevens, R. L., & Hereld, M. (2007) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e318039b4de · Cited by 43 · see citing works
Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON
Lytton, W. W., Seidenstein, A. H., Durá-Bernal, S., McDougal, R. A., Schürmann, F., & Hines, M. L. (2016) · Neural Computation · doi:10.1162/neco_a_00876 · Cited by 43 · see citing works
Self-citationCode Generation in Computational Neuroscience: A Review of Tools and Techniques
Blundell, I., Brette, R., Cleland, T. A., Close, T., Coca, D., Davison, A. P., Diaz-Pier, S., Musoles, C. F., Gleeson, P., Goodman, D. F. M., et al. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00068 · Cited by 43 · see citing works
Self-citationIndependent Variable Time-Step Integration of Individual Neurons for Network Simulations
Lytton, W. W., & Hines, M. L. (2005) · Neural Computation · doi:10.1162/0899766053429453 · Cited by 43 · see citing works
Self-citationComparison of neuronal spike exchange methods on a Blue Gene/P supercomputer
Hines, M. L. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00049 · Cited by 42 · see citing works
Self-citationSrikanth, S., & Narayanan, R. (2015) · eNeuro · doi:10.1523/eneuro.0053-15.2015 · Cited by 41 · see citing works
Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware
Rast, A. D., Galluppi, F., Davies, S., Plana, L. A., Patterson, C., Sharp, T., Lester, D., & Furber, S. B. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.06.014 · Cited by 39 · see citing works
A GPU-based computational framework that bridges neuron simulation and artificial intelligence
Zhang, Y., He, G., Ма, Л., Liu, X., Hjorth, J. J. J., Kozlov, A., He, Y., Zhang, S., Kotaleski, J. H., Tian, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41553-7 · Cited by 38 · see citing works
Durá-Bernal, S., Neymotin, S. A., Kerr, C. C., Sivagnanam, S., Majumdar, A., Francis, J. T., & Lytton, W. W. (2017) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2656758 · Cited by 39 · see citing works
Self-citationEarly experiences in developing and managing the neuroscience gateway
Sivagnanam, S., Majumdar, A., Yoshimoto, K. K., Astakhov, V., Bandrowski, A. E., Martone, M. E., & Carnevale, N. T. (2014) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3283 · Cited by 39 · see citing works
Limits to the development of feed-forward structures in large recurrent neuronal networks
Kunkel, S. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00160 · Cited by 38 · see citing works
Hines, M. L., Carnevale, T., & McDougal, R. A. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_795 · Cited by 37 · see citing works
Self-citationHendrickson, P. J., Yu, G. J., Song, D. B., & Berger, T. W. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2445771 · Cited by 36 · see citing works
Coggan, J. S., Calì, C., Keller, D. X., Agus, M., Boges, D. J., Abdellah, M., Kare, K., Lehväslaiho, H., Eilemann, S., Jolivet, R., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00664 · Cited by 36 · see citing works
Self-citationTonic-Clonic Transitions in Computer Simulation
Lytton, W. W., & Omurtag, A. (2007) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3180336fc0 · Cited by 36 · see citing works
Self-citationScalable event-driven native parallel processing
Rast, A. D., Jin, X., Galluppi, F., Plana, L. A., Patterson, C., & Furber, S. B. (2010) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/1787275.1787279 · Cited by 36 · see citing works
Beining, M., Mongiat, L. A., Schwarzacher, S. W., Cuntz, H., & Jedlička, P. (2017) · eLife · doi:10.7554/elife.26517 · Cited by 35 · see citing works
Carnevale, T. (2007) · Scholarpedia · doi:10.4249/scholarpedia.1378 · Cited by 34 · see citing works
Just-in-Time Connectivity for Large Spiking Networks
Lytton, W. W., Omurtag, A., Neymotin, S. A., & Hines, M. L. (2008) · Neural Computation · doi:10.1162/neco.2008.10-07-622 · Cited by 33 · see citing works
Self-citationA forecast-based STDP rule suitable for neuromorphic implementation
Davies, S., Galluppi, F., Rast, A. D., & Furber, S. B. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.018 · Cited by 31 · see citing works
Constructing Neuronal Network Models in Massively Parallel Environments
Ippen, T., Eppler, J. M., Pleßer, H. E., & Diesmann, M. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00030 · Cited by 31 · see citing works
A Component-Based Extension Framework for Large-Scale Parallel Simulations in NEURON
King, J. G. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.010.2009 · Cited by 30 · see citing works
Kumar, S., Heidelberger, P., Chen, D., & Hines, M. L. (2010) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2010.5470407 · Cited by 29 · see citing works
Self-citationComputational modeling of epilepsy
Bezaire, M. J., & Soltész, I. (2011) · Epilepsia · doi:10.1111/j.1528-1167.2011.03225.x · Cited by 27 · see citing works
Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform
Chen, G., Bing, Z., Röhrbein, F., Conradt, J., Huang, K., Cheng, L., Jiang, Z., & Knoll, A. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2712712 · Cited by 26 · see citing works
Simplicity and Efficiency of Integrate-and-Fire Neuron Models
Pleßer, H. E., & Diesmann, M. (2008) · Neural Computation · doi:10.1162/neco.2008.03-08-731 · Cited by 24 · see citing works
Deistler, M., Kadhim, K. L., Pals, M., Beck, J., Huang, Z., Gloeckler, M., Lappalainen, J. K., Schröder, C., Berens, P., Gonçalves, P. J., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02895-w · Cited by 30 · see citing works
Linking Macroscopic with Microscopic Neuroanatomy Using Synthetic Neuronal Populations
Schneider, C. J., Cuntz, H., & Soltész, I. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003921 · Cited by 23 · see citing works
Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE
Borges, F. S., Moreira, J. V. S., Takarabe, L. M., Lytton, W. W., & Durá-Bernal, S. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.884245 · Cited by 23 · see citing works
Self-citationDegeneracy in the emergence of spike-triggered average of hippocampal pyramidal neurons
Jain, A., & Narayanan, R. (2020) · Scientific Reports · doi:10.1038/s41598-019-57243-8 · Cited by 22 · see citing works
García-Grajales, J. A., Rucabado, G., García-Dopico, A., Peña, J.-M., & Jérusalem, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0116532 · Cited by 21 · see citing works
PAX: A mixed hardware/software simulation platform for spiking neural networks
Renaud, S., Tomas, J., Lewis, N., Bornat, Y., Daouzli, A., Rudolph, M., Alain Destexhe, A., & Saïghi, S. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.02.006 · Cited by 21 · see citing works
PyRates—A Python framework for rate-based neural simulations
Gast, R., Rose, D., Salomon, C. J., Möller, H. E., Weiskopf, N., & Knösche, T. R. (2019) · PLoS ONE · doi:10.1371/journal.pone.0225900 · Cited by 21 · see citing works
Neural Simulations on Multi-Core Architectures
Eichner, H. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.021.2009 · Cited by 20 · see citing works
Efficient phase coding in hippocampal place cells
Seenivasan, P., & Narayanan, R. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033393 · Cited by 21 · see citing works
Sandberg, A., Bostrom, N., & Martin, J. (2008) · Cited by 20 · see citing works
Tejada, J., & Roque, A. C. (2014) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2014.02.007 · Cited by 19 · see citing works
Accelerating compartmental modeling on a graphical processing unit
Ben‐Shalom, R., Liberman, G., & Korngreen, A. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00004 · Cited by 19 · see citing works
Hines, M. L., Carnevale, T., & McDougal, R. A. (2019) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_795-2 · Cited by 19 · see citing works
Self-citationHendrickson, P. J., Yu, G. J., Song, D., & Berger, T. W. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00155 · Cited by 18 · see citing works
Recent Advances and New Frontiers in Spiking Neural Networks
Zhang, D., Zhang, T., Jia, S., Wang, Q., & Xu, B. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/790 · Cited by 18 · see citing works
Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers
Lansner, A., & Diesmann, M. (2012) · Springer eBooks · doi:10.1007/978-94-007-3858-4_10 · Cited by 18 · see citing works
NEST by Example: An Introduction to the Neural Simulation Tool NEST
Gewaltig, M.-O., Morrison, A., & Pleßer, H. E. (2012) · Springer eBooks · doi:10.1007/978-94-007-3858-4_18 · Cited by 18 · see citing works
Parallel architecture and optimization for discrete-event simulation of spike neural networks
Tang, Y., Zhang, B., Wu, J., Hu, T., Zhou, J., & Liu, F. (2012) · Science China Technological Sciences · doi:10.1007/s11431-012-5084-2 · Cited by 17 · see citing works
Schürmann, F., Delalondre, F., Kumbhar, P., Biddiscombe, J., Gila, M., Tacchella, D., Curioni, A., Metzler, B., Morjan, P., Fenkes, J., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07518-1_21 · Cited by 16 · see citing works
The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code
Kunkel, S., & Schenck, W. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00040 · Cited by 16 · see citing works
Silicon-Based Dynamic Synapse With Depressing Response
Dowrick, T., Hall, S., & Mcdaid, L. J. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2211035 · Cited by 16 · see citing works
Plagge, M., Carothers, C. D., Gonsiorowski, E. J., & McGlohon, N. (2018) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/3186317 · Cited by 16 · see citing works
A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture
Cornelis, H., Coop, A. D., & Bower, J. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0028956 · Cited by 15 · see citing works
Decision making under uncertainty in a spiking neural network model of the basal ganglia
Héricé, C., Khalil, R., Moftah, M. Z., Boraud, T., Guthrie, M., & Garenne, A. (2016) · Journal of Integrative Neuroscience · doi:10.1142/s021963521650028x · Cited by 14 · see citing works
Galiautdinov, R., & Mkrttchian, V. S. (2019) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-1581-5.ch015 · Cited by 14 · see citing works
Cao, Y., Zhao, C., Zhao, T., Sun, Y., Liu, Z., Li, X., Yin, L., Gu, J., Ren, H., Geng, X., et al. (2023) · Journal of Materials Chemistry C · doi:10.1039/d2tc04745d · Cited by 14 · see citing works
A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
Albers, J., Pronold, J., Kurth, A. C., Vennemo, S. B., Mood, K. H., Patronis, A., Terhorst, D., Jordan, J., Kunkel, S., Tetzlaff, T., et al. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.837549 · Cited by 14 · see citing works
Simulations of cortical networks using spatially extended conductance‐based neuronal models
Haufler, D., Ito, S., Christof Koch, A., & Arkhipov, A. S. (2022) · The Journal of Physiology · doi:10.1113/jp284030 · Cited by 12 · see citing works
Integrating Brain Structure and Dynamics on Supercomputers
van Albada, S. J., Kunkel, S., Morrison, A., & Diesmann, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12084-3_3 · Cited by 13 · see citing works
Lytton, W. W., Neymotin, S. A., & Hines, M. L. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.03.005 · Cited by 12 · see citing works
Self-citationThe dynamical analysis of modified two-compartment neuron model and FPGA implementation
Lin, Q., Wang, J., Yang, S., Yi, G., Deng, B., Wei, X., & Yu, H. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.04.165 · Cited by 12 · see citing works
Anatomically Detailed and Large-Scale Simulations Studying Synapse Loss and Synchrony Using NeuroBox
Breit, M., Stepniewski, M., Grein, S., Gottmann, P., Reinhardt, L., & Queisser, G. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00008 · Cited by 12 · see citing works
Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System
Rast, A. D., Navaridas, J., Jin, X., Galluppi, F., Plana, L. A., Miguel-Alonso, J., Patterson, C., Luján, M., & Furber, S. B. (2011) · International Journal of Parallel Programming · doi:10.1007/s10766-011-0180-7 · Cited by 11 · see citing works
Eilemann, S., Delalondre, F., Bernard, J., Planas, J., Schuermann, F., Biddiscombe, J., Bekas, C., Curioni, A., Metzler, B., Kaltstein, P., et al. (2016) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2016.23 · Cited by 11 · see citing works
Compartmental neural simulations with spatial adaptivity
Rempe, M. J., Spruston, N., Kath, W. L., & Chopp, D. L. (2008) · Journal of Computational Neuroscience · doi:10.1007/s10827-008-0089-3 · Cited by 11 · see citing works
Finite-difference schemes for parabolic problems on graphs
Čiegis, R., & Tumanova, N. (2010) · Lithuanian Mathematical Journal · doi:10.1007/s10986-010-9077-1 · Cited by 10 · see citing works
- Authors
- Migliore, M., Cannia, C., Lytton, W. W., Markram, H., & Hines, M. L.
- Affiliations
- National Research Council (IT)
- State University of New York (US)
- École Polytechnique Fédérale de Lausanne (CH)
- Yale University (US)
- Corresponding author
- Migliore, M.
- Venue
- Journal of Computational Neuroscience
- Published
- 2006-05-26
- Volume
- 21
- Issue
- 2
- Pages
- 119-129
- ISSN
- 0929-5313 (ISSN-L)
- 1573-6873
- PMC
- PMC2655137
- OpenAlex
- W2170402833
- Cited by
- 196
- References
- 20
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 7 · 2023: 4 · 2022: 12 · 2021: 2 · 2020: 10 · 2019: 10 · 2018: 5 · 2017: 9 · 2016: 19 · 2015: 12 · 2014: 14 · 2013: 8 · 2012: 14
- FWCI
- 21.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Computational Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- Yale University
- École Polytechnique Fédérale de Lausanne
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (NS11613, R01NS045612, NS045612, R01NS011613)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Keywords
- spiking neural networks, superlinear speedup, Beowulf cluster, network models, computational neuroscience
- MeSH terms
- Action Potentials, Algorithms, Animals, Cerebral Cortex, Humans, Nerve Net, Neural Pathways, Synaptic Transmission, Neurons, Software, 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
