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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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
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
Arbib MA, Grethe JS (2001) Computing the brain: a guide to neuroinformatics. Academic, San Diego
Babajani-Feremi A, Soltanian-Zadeh H (2010) Multi-area neural mass modeling of EEG and MEG signals. Neuroimage 52(3):793–811
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
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
Binzegger T, Douglas RJ, Martin KAC (2004) A quantitative map of the circuit of cat primary visual cortex. J Neurosci 39(24):8441–8453
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
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
Brette R (2007) Exact simulation of integrate-and-fire models with exponential currents. Neural Comput 19(10):2604–2609
Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94(5):3637–3642
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
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
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
Brunel N (2000) Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8(3):183–208
Brunel N, van Rossum MCW (2007) Lapicque’s 1907 paper: from frogs to integrate-and-fire. Biol Cybern 97:337–339
Buxton RB, Uludag K, Dubowitz DJ, Liu TT (2004) Modeling the hemodynamic response to brain activation. Neuroimage 23(Supplement 1):S220–S233
Cannon R, Gewaltig M-O (2011) Fostering sustainability of software tools in neuroscience. arXiv:1205.3025v1 [q-bio.NC]
Chemla S, Chavane F (2010) Voltage-sensitive dye imaging: technique review and models. J Physiol Paris 104(1–2):40–50
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
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
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
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
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
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
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
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
Douglas R, Mahovald M, Mead C (1995) Neuromorphic analogue VLSI. Ann Rev Neurosci 18(255–281)
Ermentrout GB, Kopell N (1984) Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM J Appl Math 44:1133–1149
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
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
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
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
Göbel W, Helmchen F (2007) In vivo calcium imaging of neural network function. Physiology 22:358–365
Grün S (2009) Data-driven significance estimation for precise spike correlation. J Neurophysiol 101:1126–1140
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
Hammarlund P, Ekeberg Ö (1998) Large neural network simulations on multiple hardware platforms. J Comp Neurosci 5:443–459
Hansel D, Mato G, Meunier C (1995) Synchrony in excitatory neural networks. Neural Comp 7(2):307–337
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
Hines ML, Markram H, Schürmann F (2008) Fully implicit parallel simulation of single neurons. J Comput Neurosci 25(3):439–448
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
Indiveri G, Chicca E, Douglas RJ (2009) Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cogn Comput 1:119–127
Izhikevich EM (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 5(15):1063–1070
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
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
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
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
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)
Lansner A (2009) Associative memory models – from cell assembly theory to biophysically detailed cortex simulations. Trends Neurosci 32:178–186
Lansner A, Fransén E (1992) Modeling Hebbian cell assemblies comprised of cortical neurons. Netw Comput Neural Syst 3:105–119
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
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
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
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
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
MacGregor RJ (1987) Neural and brain modeling. Academic, San Diego
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
Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160
Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006) Parallel network simulations with NEURON. J Comput Neurosci 21(2):119–223
Misra J, Saha I (2010) Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74:239–255
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
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
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
Morrison A, Aertsen A, Diesmann M (2007a) Spike-timing dependent plasticity in balanced random networks. Neural Comput 19:1437–1467
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
Morrison A, Diesmann M, Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike-timing. Biol Cybern 98:459–478
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
Noble D (2006) The music of life: biology beyond genes. Oxford University Press, Oxford
Nordlie E, Gewaltig M-O, Plesser HE (2009) Towards reproducible descriptions of neuronal network models. PLoS Comput Biol 5(8):e1000456
Plesser HE, Diesmann M (2009) Simplicity and efficiency of integrate-and-fire neuron models. Neural Comput 21:353–359
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]
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
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
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
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
Sotero RC, Trujillo-Barreto NJ (2008) Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism. Neuroimage 39(1):290–309
Stettler DD, Axel R (2009) Representations of Odor in the Piriform Cortex. Neuron 63: 854–864
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
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
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
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
van Vreeswijk C, Sompolinsky H (1996) Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274:1724–1726
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
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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
DOI: https://doi.org/10.1007/978-94-007-3858-4_10
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-3857-7
Online ISBN: 978-94-007-3858-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)