Skip to main content
SpringerLink
Log in

Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers

  • Chapter
  • First Online:
Book cover Computational Systems Neurobiology

Abstract

The number of neurons and synapses in biological brains is very large, on the order of millions and billions respectively even in small animals like insects and mice. By comparison most neuronal network models developed and simulated up to now have been tiny, comprising many orders of magnitude less neurons than their real counterpart, with an even more dramatic difference when it comes to the number of synapses. In this chapter we discuss why and when it may be important to work with large-scale, if not full-scale, neuronal network and brain models and to run simulations on supercomputers. We describe the state-of-the-art in large-scale neural simulation technology and methodology as well as ways to analyze and visualize output from such simulations. Finally we discuss the challenges and future trends in this field.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  • Amit DJ, Brunel N (1997) Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb Cortex 7:237–252

    Article  PubMed  CAS  Google Scholar 

  • Ananthanarayanan R, Esser SK, Simon HD, Modha DS (2009) The cat is out of the bag: cortical simulations with 109neurons, 1013synapses. In: Proceedings of the conference on high performance networking and computing (SC09), Portland, Oregon, USA:Article no. 63. ACM New York, NY, USA

    Google Scholar 

  • Arbib MA, Grethe JS (2001) Computing the brain: a guide to neuroinformatics. Academic, San Diego

    Google Scholar 

  • Babajani-Feremi A, Soltanian-Zadeh H (2010) Multi-area neural mass modeling of EEG and MEG signals. Neuroimage 52(3):793–811

    Article  PubMed  Google Scholar 

  • Bailey J, Hammerstrom D (1988) Why VLSI implementations of associative VLCNs require connection multiplexing. International Conference on Neural Networks, San Diego, USA 2, pp 173–180

    Google Scholar 

  • Beaulieu C, Colonnier M (1983) The number of neurons in the different laminae of the binocular and monocular regions of area 17 in the cat. J Comp Neurology 217:337–344

    Article  CAS  Google Scholar 

  • Binzegger T, Douglas RJ, Martin KAC (2004) A quantitative map of the circuit of cat primary visual cortex. J Neurosci 39(24):8441–8453

    Article  Google Scholar 

  • Boahen KA (2000) Point-to-point connectivity between neuromorphic chips using address events. IEEE Trans Circuits Syst II: Analog Digit Signal Process 47:416–434

    Article  Google Scholar 

  • Bojak I, Oostendorp TF, Reid AT, Kötter R (2010) Connecting mean field models of neural activity to EEG and fMRI data. Brain Topogr 23:139–149

    Article  PubMed  Google Scholar 

  • Brette R (2007) Exact simulation of integrate-and-fire models with exponential currents. Neural Comput 19(10):2604–2609

    Article  PubMed  Google Scholar 

  • Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94(5):3637–3642

    Article  PubMed  Google Scholar 

  • Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Goodman PH, Harris FC Jr, Zirpe M, Natschläger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison A, El Boustani S, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23:349–398

    Article  PubMed  Google Scholar 

  • Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC Jr, Zirpe M, Natschläger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007b) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23(3):349–398

    Article  PubMed  Google Scholar 

  • Brown A, Furber S, Reeve J, Wilson P, Zwolinski M, Chad J, Plana LA, Lester D (2010) A communication infrastructure for a million processor machine. In: Proceedings of ACM international conference on computing frontiers, Bertinoro, Italy, pp 75–76

    Google Scholar 

  • Brunel N (2000) Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8(3):183–208

    Article  PubMed  CAS  Google Scholar 

  • Brunel N, van Rossum MCW (2007) Lapicque’s 1907 paper: from frogs to integrate-and-fire. Biol Cybern 97:337–339

    Article  PubMed  Google Scholar 

  • Buxton RB, Uludag K, Dubowitz DJ, Liu TT (2004) Modeling the hemodynamic response to brain activation. Neuroimage 23(Supplement 1):S220–S233

    Article  PubMed  Google Scholar 

  • Cannon R, Gewaltig M-O (2011) Fostering sustainability of software tools in neuroscience. arXiv:1205.3025v1 [q-bio.NC]

    Google Scholar 

  • Chemla S, Chavane F (2010) Voltage-sensitive dye imaging: technique review and models. J Physiol Paris 104(1–2):40–50

    Article  PubMed  CAS  Google Scholar 

  • Cheung K, Schultz SR, Leong PHW (2009) A parallel spiking neural network simulator. In: International conference on Field-Programmable Technology (FPT’09), Sydney, NSW, pp 247–254

    Google Scholar 

  • Davison AP, Brüderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P (2009) PyNN: a common interface for neuronal network simulators. Front Neuroinform 2(11). doi:10.3389/neuro.11.011.2008

  • De Schutter E (2008) Why are computational neuroscience and systems biology so separate? PLoS Comput Biol 4(5):e1000078

    Article  PubMed  Google Scholar 

  • Dehghani N, Bédard C, Cash S, Halgren E, Destexhe A (2010) Comparative power spectral analysis of simultaneous electroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media: EEG and MEG power spectra. J Comput Neurosci 29(3):405–421

    Article  PubMed  Google Scholar 

  • D’Haene M, Schrauwen B (2010) Fast and exact simulation methods applied on a broad range of neuron models. Neural Comput 22(6):1468–1472

    Article  PubMed  Google Scholar 

  • Diesmann M, Gewaltig M-O (2002) NEST: An environment for neural systems simulations. Forschung und wisschenschaftliches Rechnen, Beitraege zum Heinz-Billing-Preis 2001. In: Plesser T, Macho V (eds). Göttingen, Ges fuer Wiss Datenverarbeitung: 43–70

    Google Scholar 

  • Djurfeldt M (2012) The connection-set algebra–a novel formalism for the representation of connectivity structure in neuronal Network models. Neuroinformatics. doi: 10.1007/s12021-012-9146-1. Online first

  • Djurfeldt M, Lansner A (2007) Workshop report: 1st INCF workshop on large-scale modeling of the nervous system, Nature Precedings, Stockholm, Sweden

    Google Scholar 

  • Djurfeldt M, Lundqvist M, Johansson C, Rehn M, Ekeberg Ö, Lansner A (2008) Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer. IBM J Res Dev 52:31–41

    Article  Google Scholar 

  • Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Hellgren Kotaleski J, Ekeberg Ö (2010) Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics 8:43–60

    Article  PubMed  Google Scholar 

  • Douglas R, Mahovald M, Mead C (1995) Neuromorphic analogue VLSI. Ann Rev Neurosci 18(255–281)

    Google Scholar 

  • Ermentrout GB, Kopell N (1984) Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM J Appl Math 44:1133–1149

    Article  Google Scholar 

  • Fidjeland AK, Shanahan MP (2010) Accelerated simulation of spiking neural networks using GPUs. IEEE International Joint Conference on Neural Networks (IJCNN), Barcelona. IEEE, 345 E 47TH ST, New York, NY 10017

    Google Scholar 

  • Fourcaud-Trocmé N, Hansel D, van Vreeswijk C, Brunel N (2003) How spike generation mechanisms determine the neuronal response to fluctuating inputs. J Neurosci 23:11628–11640

    PubMed  Google Scholar 

  • Gabbott PL, Somogyi P (1986) Quantitative distribution of GABA-immunoreactive neurons in the visual cortex (area 17) of the cat. Exp Brain Res 61(2):323–331

    PubMed  CAS  Google Scholar 

  • Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, Farinella M, Morse TM, Davison A, Ray S, Bhalla US, Barnes SR, Dimitrova YD, Silver RA (2010) NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput Biol 6:e1000815

    Article  PubMed  Google Scholar 

  • Göbel W, Helmchen F (2007) In vivo calcium imaging of neural network function. Physiology 22:358–365

    Article  PubMed  Google Scholar 

  • Grün S (2009) Data-driven significance estimation for precise spike correlation. J Neurophysiol 101:1126–1140

    Article  PubMed  Google Scholar 

  • Guerrero-Rivera R, Morrison A, Diesmann M, Pearce TC (2006) Programmable logic construction kits for hyper-real-time neuronal modeling. Neural Comput 18(11):2651–2679

    Article  PubMed  Google Scholar 

  • Hammarlund P, Ekeberg Ö (1998) Large neural network simulations on multiple hardware platforms. J Comp Neurosci 5:443–459

    Article  CAS  Google Scholar 

  • Hansel D, Mato G, Meunier C (1995) Synchrony in excitatory neural networks. Neural Comp 7(2):307–337

    Article  CAS  Google Scholar 

  • Hanuschkin A, Kunkel S, Helias M, Morrison A, Diesmann M (2010) A general and efficient method for incorporating precise spike times in globally time-driven simulations. Front Neuroinform 4:113

    Article  PubMed  Google Scholar 

  • Hines ML, Markram H, Schürmann F (2008) Fully implicit parallel simulation of single neurons. J Comput Neurosci 25(3):439–448

    Article  PubMed  Google Scholar 

  • Homma R, Baker B, Jin L, Garaschuk O, Konnerth A, Cohen L, Bleau C, Canepari M, Djurisic M, Zecevic D (2009) Wide-field and two-photon imaging of brain activity with voltage- and calcium-sensitive dyes. Methods Mol Biol 489:43–79

    Article  PubMed  CAS  Google Scholar 

  • Indiveri G, Chicca E, Douglas RJ (2009) Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cogn Comput 1:119–127

    Article  Google Scholar 

  • Izhikevich EM (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 5(15):1063–1070

    Article  Google Scholar 

  • Izhikevich E (2006) 2011, http://www.izhikevich.org/human_brain_simulation/Blue_Brain.htm

  • Jin X, Galluppi F, Patterson C, Rast A, Davies S, Temple S, Furber S (2010a) Algorithm and software for simulation of spiking neural networks on the multi-chip SpiNNaker system. International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, 18–23 July 2010, pp 649–656

    Google Scholar 

  • Jin X, Lujan M, Plana LA, Davies S, Temple S, Furber S (2010b) Modeling spiking neural networks on SpiNNaker. Comput Sci Eng 12(5):91–97

    Article  Google Scholar 

  • Jo SH, Chang T, Ebong I, Bhadviya BB, Mazumder P, Lu W (2010) Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett 10:1297–1301

    Article  PubMed  CAS  Google Scholar 

  • King JG, Hines M, Hill S, Goodman PH, Markram H, Schürmann F (2009) A component-based extension framework for large-scale parallel simulations in NEURON. Front Neuroinform 3:1–11

    Article  CAS  Google Scholar 

  • Kobayashi R, Tsubo Y, Shinomoto S (2009) Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Front Comput Neurosci 3(9)

    Google Scholar 

  • Lansner A (2009) Associative memory models – from cell assembly theory to biophysically detailed cortex simulations. Trends Neurosci 32:178–186

    Article  PubMed  CAS  Google Scholar 

  • Lansner A, Fransén E (1992) Modeling Hebbian cell assemblies comprised of cortical neurons. Netw Comput Neural Syst 3:105–119

    Article  Google Scholar 

  • Likharev K, Mayr A, Muckra I, Turel O (2003) CrossNets high-performance neuromorphic architectures for CMOL circuits. Molecular electronics III. In: Reimers J, Picconatto C, Ellenbogen J, Shashidhar R (eds) New York, NY Acad Sci 1006: 146–163

    Google Scholar 

  • Lindén H, Pettersen KH, Einevoll GT (2010) Intrinsic dendritic filtering gives low-pass power spectra of local field potentials. J Comput Neurosci 29(3):423–444

    Article  PubMed  Google Scholar 

  • Lundqvist M, Rehn M, Djurfeldt M, Lansner A (2006) Attractor dynamics in a modular network model of the neocortex. Netw Comput Neural Syst 17:253–276

    Article  Google Scholar 

  • Lundqvist M, Compte A, Lansner A (2010a) Bistable, irregular firing and population oscillations in a modular attractor memory network. PLoS Comput Biol 6(6):1–12

    Article  Google Scholar 

  • Lundqvist M, Herman P, Lansner A (2010b) Theta and gamma power increases and alpha/beta power decreases with memory load in an attractor network model. J Cogn Neurosci 23(10):3008–3020

    Article  Google Scholar 

  • MacGregor RJ (1987) Neural and brain modeling. Academic, San Diego

    Google Scholar 

  • Maguire LP, McGinnity TM, Glackin B, Ghani A, Belatreche A, Harkin J (2007) Challenges for large-scale implementations of spiking neural networks on FPGAs. Neurocomputing 71:13–29

    Article  Google Scholar 

  • Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160

    Article  PubMed  CAS  Google Scholar 

  • Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006) Parallel network simulations with NEURON. J Comput Neurosci 21(2):119–223

    Article  PubMed  CAS  Google Scholar 

  • Misra J, Saha I (2010) Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74:239–255

    Article  Google Scholar 

  • Morrison A, Diesmann M (2008) Maintaining causality in discrete time neuronal network simulations. In: Beim Graben P, Zhou C, Thiel M, Kurths J (eds) Lectures in supercomputational neuroscience: dynamics in complex brain networks. Springer, Berlin, pp 267–278

    Chapter  Google Scholar 

  • Morrison A, Mehring C, Geisel T, Aertsen A, Diesmann M (2005) Advancing the boundaries of high connectivity network simulation with distributed computing. Neural Comput 17(8): 1776–1801

    Article  PubMed  Google Scholar 

  • Morrison A, Mehring C, Geisel T, Aertsen A, Diesmann M (2005b) Advancing the boundaries of high connectivity network simulation with distributed computing. Neural Comput 17(8): 1776–1801

    Article  PubMed  Google Scholar 

  • Morrison A, Aertsen A, Diesmann M (2007a) Spike-timing dependent plasticity in balanced random networks. Neural Comput 19:1437–1467

    Article  PubMed  Google Scholar 

  • Morrison A, Straube S, Plesser HE, Diesmann M (2007b) Exact subthreshold integration with continuous spike times in discrete time neural network simulations. Neural Comput 19(1): 47–79

    Article  PubMed  Google Scholar 

  • Morrison A, Diesmann M, Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike-timing. Biol Cybern 98:459–478

    Article  PubMed  Google Scholar 

  • Nageswarana JM, Dutt N, Krichmar JL, Nicolau A, Veidenbaum AV (2009) A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors. Neural Netw 22:791–800

    Article  Google Scholar 

  • Noble D (2006) The music of life: biology beyond genes. Oxford University Press, Oxford

    Google Scholar 

  • Nordlie E, Gewaltig M-O, Plesser HE (2009) Towards reproducible descriptions of neuronal network models. PLoS Comput Biol 5(8):e1000456

    Article  PubMed  Google Scholar 

  • Plesser HE, Diesmann M (2009) Simplicity and efficiency of integrate-and-fire neuron models. Neural Comput 21:353–359

    Article  PubMed  Google Scholar 

  • Potjans T, Diesmann M (2011) The cell-type specific connectivity of the local cortical network explains prominent features of neuronal activity. arXiv:1106.5678 [q-bio.NC]

    Google Scholar 

  • Potjans W, Morrison A, Diesmann M (2010) Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Front Comput Neurosci 4(141). doi:10.3389/fncom.2010.00141

  • Rasch M, Logothetis NK, Kreiman G (2009) From neurons to circuits: linear estimation of local field potentials. J Neurosci 29:13785–13796

    Article  PubMed  CAS  Google Scholar 

  • Rotter S, Diesmann M (1999) Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biol Cybern 81(5/6):381–402

    Article  PubMed  CAS  Google Scholar 

  • Schemmel J, Brüderle D, Meier K, Ostendorf B (2007) Modeling synaptic plasticity within networks of highly accelerated I&F neurons. In: IEEE international symposium on circuits and systems art. no. 4253401, pp 3367–3370

    Google Scholar 

  • Schemmel J, Brüderle D, Grübl A, Hock M, Meier K, Millner S (2010) A wafer-scale neuromorphic hardware system for large-scale neural modeling. In: ISCAS 2010 – IEEE International Symposium on Circuits and Systems: nano-bio circuit fabrics and systems, pp 1947–1950

    Google Scholar 

  • Sotero RC, Trujillo-Barreto NJ (2008) Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism. Neuroimage 39(1):290–309

    Article  PubMed  Google Scholar 

  • Stettler DD, Axel R (2009) Representations of Odor in the Piriform Cortex. Neuron 63: 854–864

    Article  PubMed  CAS  Google Scholar 

  • Thomas D, Luk W (2009) FPGA accelerated simulation of biologically plausible spiking neural networks. In: 17th IEEE symposium on field programmable custom computing machines, Napa, California, pp 45–52

    Google Scholar 

  • Thomson AM, West DC, Wang Y, Bannister AP (2002) Synaptic connections and small circuits involving excitatory and inhibitory neurons in layer 2-5 of adult rat and cat neocortex: triple intracellular recordings and biocytin labelling in vitro. Cereb Cortex 12:936–953

    Article  PubMed  Google Scholar 

  • Traub RD, Whittington MA, Buhl EH, Jefferys JGR, Faulkner HJ (1999) On the mechanism of the γ – β frequency shift in neuronal oscillations induced in rat hippocampal slices by tetanic stimulation. J Neurosci 19(3):1088–1105

    PubMed  CAS  Google Scholar 

  • van Elburg RAJ, van Ooyen A (2009) Generalization of the event-based Carnevale-Hines integration scheme for integrate-and-fire models. Neural Comput 21(7):1913–1930. doi:10.1162/neco.2009.07-08-815

    Article  PubMed  Google Scholar 

  • van Vreeswijk C, Sompolinsky H (1996) Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274:1724–1726

    Article  PubMed  Google Scholar 

  • Winograd T (1975) Breaking the complexity barrier (again). ACM SIGPLAN Notices 10:1 (Jan. 1975) (jointly issued as SIGIR FORUM 9:3 Winter 1974), pp 13–30. Reprinted. In: Barstow D, Shrobe H, Sandewall E (eds) Interactive programming environments. McGraw Hill, New York 1984 pp 3–18

    Google Scholar 

Download references

Acknowledgements

Partially funded by EU Grant 15879 (FACETS), BMBF Grant 01GQ0420 to the Bernstein Center Freiburg, Next-Generation Supercomputer Project of MEXT, the Helmholtz Alliance on Systems Biology, the Swedish Science Council (VR-621-2004-3807), VINNOVA (Swedish Governmental Agency for Innovation Systems), the Swedish Foundation for Strategic Research (through the Stockholm Brain Institute), and the European Union grants 15879 (FACETS) and 269921 (BrainScaleS). Access to supercomputing facility through JUGENE-Grant JINB33. We also thank the DEISA Consortium (www.deisa.eu), co-funded through the EU FP6 project RI-031513 and the FP7 project RI-222919, for support within the DEISA Extreme Computing Initiative.

Author information

Authors and Affiliations

  1. Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden

    Anders Lansner

  2. Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden

    Anders Lansner

  3. Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Juelich, Juelich, Germany

    Markus Diesmann

  4. Faculty of Medicine, RWTH Aachen University, Aachen, Germany

    Markus Diesmann

  5. RIKEN Brain Science Institute, Wako City, Japan

    Markus Diesmann

Authors
  1. Anders Lansner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Markus Diesmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anders Lansner .

Editor information

Editors and Affiliations

  1. European Bioinformatics Institute, EMBL-Outstation-Hinxton, Wellcome Trust Genome Campus, Welcome-Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom

    N. Le Novère

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Lansner, A., Diesmann, M. (2012). Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers. In: Le Novère, N. (eds) Computational Systems Neurobiology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-3858-4_10

Download citation

Publish with us

Policies and ethics

Search

Navigation