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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 182145, 10 pages
http://dx.doi.org/10.1155/2013/182145
Research Article

Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

1Department of Pediatrics, The University of Chicago, Chicago, IL 60637, USA
2Computation Institute, The University of Chicago and Argonne National Laboratories, Argonne, IL 60439, USA
3Mathematics and Computer Science Division, Argonne National Laboratories, IL 60439, USA
4Department of Neurology, The University of Chicago, Chicago, IL 60637, USA
5Committee on Computational Neuroscience, The University of Chicago, Chicago, IL 60637, USA

Received 22 August 2013; Accepted 3 November 2013

Academic Editor: Qingshan Liu

Copyright © 2013 Lorenzo L. Pesce et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. L. Banks, “Statistical data mining,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 1, pp. 9–25, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. B. Efron, “Large-scale simultaneous hypothesis testing: the choice of a null hypothesis,” Journal of the American Statistical Association, vol. 99, no. 465, pp. 96–104, 2004. View at Google Scholar · View at Scopus
  3. H. Akil, M. E. Martone, and D. C. Van Essen, “Challenges and opportunities in mining neuroscience data,” Science, vol. 331, no. 6018, pp. 708–712, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Brette, M. Rudolph, T. Carnevale et al., “Simulation of networks of spiking neurons: a review of tools and strategies,” Journal of Computational Neuroscience, vol. 23, no. 3, pp. 349–398, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Gerstner, H. Sprekeler, and G. Deco, “Theory and simulation in neuroscience,” Science, vol. 338, no. 6103, pp. 60–65, 2012. View at Google Scholar
  6. W. Van Drongelen, H. C. Lee, M. Hereld, Z. Chen, F. P. Elsen, and R. L. Stevens, “Emergent epileptiform activity in neural networks with weak excitatory synapses,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 2, pp. 236–241, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Djurfeldt, M. Lundqvist, C. Johansson, M. Rehn, Ö. Ekeberg, and A. Lansner, “Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer,” IBM Journal of Research and Development, vol. 52, no. 1-2, pp. 31–42, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. B. S. Robinson, G. J. Yu, P. J. Hendrickson, D. Song, and T. W. Berger, “Implementation of activity-dependent synaptic plasticity rules for a large-scale biologically realistic model of the hippocampus,” in Proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 1366–1369, 2012.
  9. E. Phoka, M. Wildie, S. R. Schultz, and M. Barahona, “Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model,” Journal of Computational Neuroscience, vol. 33, no. 2, pp. 323–339, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Case and I. Soltesz, “Computational modeling of epilepsy,” Epilepsia, vol. 52, no. 8, pp. 12–15, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Kozloski, “Automated reconstruction of neural tissue and the role of large-scale simulation,” Neuroinformatics, vol. 9, no. 2-3, pp. 133–142, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. V. K. Jirsa and R. A. Stefanescu, “Neural population modes capture biologically realistic large scale network dynamics,” Bulletin of Mathematical Biology, vol. 73, no. 2, pp. 325–343, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. R. A. Koene, B. Tijms, P. Van Hees et al., “NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies,” Neuroinformatics, vol. 7, no. 3, pp. 195–210, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. J. M. Nageswaran, N. Dutt, J. L. Krichmar, A. Nicolau, and A. V. Veidenbaum, “A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors,” Neural Networks, vol. 22, no. 5-6, pp. 791–800, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. J. G. King, M. Hines, S. Hill, P. H. Goodman, H. Markram, and F. Schürmann, “A component-based extension framework for large-scale parallel simulations in NEURON,” Frontiers in Neuroinformatics, vol. 3, p. 10, 2009. View at Google Scholar
  16. R. D. Traub, D. Schmitz, N. Maier, M. A. Whittington, and A. Draguhn, “Axonal properties determine somatic firing in a model of in vitro CA1 hippocampal sharp wave/ripples and persistent gamma oscillations,” European Journal of Neuroscience, vol. 36, no. 5, pp. 2650–2660, 2012. View at Google Scholar
  17. W. W. Lytton, A. Omurtag, S. A. Neymotin, and M. L. Hines, “Just-in-time connectivity for large spiking networks,” Neural Computation, vol. 20, no. 11, pp. 2745–2756, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. A. P. Alivisatos, M. Chun, G. M. Church et al., “The brain activity map,” Science, vol. 339, no. 6125, pp. 1284–1285, 2013. View at Publisher · View at Google Scholar
  19. “Will technology deliver for ‘big neuroscience’?” Nature Methods, vol. 10, no. 4, pp. 271–271, 2013. View at Publisher · View at Google Scholar
  20. “The brain activity map: hard cell,” The Economist, pp. 79–80, March 2013.
  21. M. Hereld, R. L. Stevens, W. Van Drongelen, and H. C. Lee, “Developing a petascale neural simulation,” in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '04), pp. 3999–4002, September 2004. View at Scopus
  22. J. Dongarra, I. Foster, G. Fox et al., Sourcebook of Parallel Computing, Morgan Kauffman, San Francisco, Calif, USA, 2003.
  23. V. Eijkhout, E. Chow, and R. Van de Geijn, Introduction to High Performance Scientific Computing, Lulu Press, 2012, http://www.lulu.com/.
  24. M. Hereld, R. L. Stevens, J. Teller, W. van Drongelen, and H. C. Lee, “Large neural simulations on large parallel computers,” International Journal of Bioelectromagnetism, vol. 7, no. 1, pp. 44–46, 2005. View at Google Scholar
  25. M. Hereld, R. L. Stevens, H. C. Lee, and W. Van Drongelen, “Framework for interactive million-neuron simulation,” Journal of Clinical Neurophysiology, vol. 24, no. 2, pp. 189–196, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. W. Van Drongelen, H. Koch, F. P. Elsen et al., “Role of persistent sodium current in bursting activity of mouse neocortical networks in vitro,” Journal of Neurophysiology, vol. 96, no. 5, pp. 2564–2577, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Visser, H. G. E. Meijer, H. C. Lee, W. Van Drongelen, M. J. A. M. Van Putten, and S. A. Van Gils, “Comparing epileptiform behavior of mesoscale detailed models and population models of neocortex,” Journal of Clinical Neurophysiology, vol. 27, no. 6, pp. 471–478, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. R. Cossart, Y. Ikegaya, and R. Yuste, “Calcium imaging of cortical networks dynamics,” Cell Calcium, vol. 37, no. 5, pp. 451–457, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. R. C. Reid, “From functional architecture to functional connectomics,” Neuron, vol. 75, no. 2, pp. 209–217, 2012. View at Google Scholar
  30. J. M. Bower and D. Beeman, The Book of Genesis, Springer, New York, NY, USA, 1995.
  31. W. Gropp, E. Lusk, and A. Skjellum, Using MPI, MIT Press, Cambridge, Mass, USA, 2nd edition, 1999.
  32. W. Gropp, E. Lusk, and R. Thakur, Using MPI-2, MIT Press, Cambridge, Mass, USA, 1999.
  33. B. Chapman, G. Jost, and R. Van der Pas, Using OpenMP, MIT Press, Cambridge, Mass, USA, 2008.
  34. G. M. Amdahl, “Validity of the single processor approach to achieving large scale computing capabilities,” in Proceedings of the Spring Joint Computer Conference, vol. 30 of AFIPS Conference Proceedings, pp. 483–485, 1967. View at Publisher · View at Google Scholar