The cat is out of the bag: Cortical simulations with 109 neurons, 1013 synapses

Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, SC '09

Volumn , Issue , 2009, Pages

  Ananthanarayanan, Rajagopal ^a   Esser, Steven K ^a   Simon, Horst D ^b   Modha, Dharmendra S ^a  

^a IBM ALMADEN RESEARCH CENTER   (United States)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GRAY MATTER; INDIVIDUAL LEARNING; MAIN MEMORY; MEMORY CAPACITY; SPIKING NEURON;

PROGRAM PROCESSORS; SUPERCOMPUTERS;

NEURONS;

EID: 74049164160     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1654059.1654124     Document Type: Conference Paper

References (36)

1. L. F. Abbott and P. Dayan. Theoretical Neuroscience. The MIT Press, Cambridge, Massachusetts, 2001.
2. R. Ananthanarayanan and D. S. Modha. Anatomy of a cortical simulator. In Supercomputing 07, 2007.
3. R. Ananthanarayanan and D. S. Modha. Scaling, stability, and synchronization in mouse-sized (and larger) cortical simulations. In CNS*2007. BMC Neurosci., 8(Suppl 2):P187, 2007.
4. R. Ananthanarayanan, R. Singh, S. Chandra, and D. S. Modha. Imaging the spatio-temporal dynamics of large-scale cortical simulations. In Society for Neuroscience, November 2008.
5. A. Bannister. Inter- and intra-laminar connections of pyramidal cells in the neocortex. Neuroscience Research, 53:95-103, 2005.
6. C. Beaulieu and M. Colonnier. A laminar analysis of the number of round-asymmetrical and flat-symmetrical synapses on spines, dendritic trunks, and cell bodies in area 17 of the cat. J. Comp. Neurol., 231(2):180-9, 1985.
7. T. Binzegger, R. J. Douglas, and K. A. Martin. A quantitative map of the circuit of cat primary visual cortex. J. Neurosci., 24(39):8441-53, 2004.
8. V. Braitenberg and A. Schüz. Cortex: Statistics and Geometry of Neuronal Conectivity. Springer, 1998.
9. D. Coates, G. Kenyon, and C. Rasmussen. A bird's-eye view of petavision, the world's first petaflop/s neural simulation.
10. A. Delorme and S. Thorpe. SpikeNET: An event-driven simulation package for modeling large networks of spiking neurons. Network: Comput. Neural Syst., 14:613:627, 2003.
11. A. Destexhe, Z. J. Mainen, and T. J. Sejnowski. Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. J. Comput. Neurosci., 1(3):195-230, 1994.
12. M. Djurfeldt, M. Lundqvist, C. Johanssen, M. Rehn, O. Ekeberg, and A. Lansner. Brain-scale simulation of the neocortex on the ibm blue gene/l supercomputer. IBM Journal of Research and Development, 52(1-2):31-42, 2007.
13. J. Frye, R. Ananthanarayanan, and D. S. Modha. Towards real-time, mouse-scale cortical simulations. In CoSyNe: Computational and Systems Neuroscience, Salt Lake City, Utah, 2007.
14. A. Gara et al. Overview of the Blue Gene/L system architecture. IBM J. Res. Devel., 49:195-212, 2005.
15. C. D. Gilbert. Circuitry, architecture, and functional dynamics of visual cortex. Cereb. Cortex, 3(5):373-86, 1993.
16. J. E. Gruner, J. C. Hirsch, and C. Sotelo. Ultrastructural features of the isolated suprasylvian gyrus in the cat. J. Comp. Neurol., 154(1):1-27, 1974.
17. IBM Blue Gene team. Overview of the IBM Blue Gene/P project. IBM J Research and Development, 52:199-220, 2008.
18. E. M. Izhikevich and G. M. Edelman. Large-scale model of mammalian thalamocortical systems. PNAS, 105:3593-3598, 2008.
19. E. M. Izhikevich, J. A. Gally, and G. M. Edelman. Spike-timing dynamics of neuronal groups. Cerebral Cortex, 14:933-944, 2004.
20. E. G. Jones. The Thalamus. Cambridge University Press, Cambridge, UK, 2007.
21. E. R. Kandel, J. H. Schwartz, and T. M. Jessell. Principles of Neural Science. McGraw-Hill Medical, 2000.
22. C. Koch. Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York, New York, 1999.
23. G. Lakner, I.-H. Chung, G. Cong, S. Fadden, N. Goracke, D. Klepacki, J. Lien, C. Pospiech, S. R. Seelam, and H.-F. Wen. IBM System Blue Gene Solution: Performance Analysis Tools. IBM Redpaper Publication, November, 2008.
24. M. Mattia and P. D. Guidice. Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Comput., 12:2305-2329, 2000.
25. H. Meuer, E. Strohmaier, J. Dongarra, and H. D. Simon. Top500 supercomputer sites. http://www.top500.org.
26. U. Mitzdorf and W. Singer. Prominent excitatory pathways in the cat visual cortex (a 17 and a 18): a current source density analysis of electrically evoked potentials. Exp. Brain Res., 33(3-4):371-394, 1978.
27. A. Morrison, C. Mehring, T. Geisel, A. D. Aertsen, and M. Diesmann. Advancing the boundaries of high-connectivity network simulation with distributed computing. Neural Comput., 17(8):1776-1801, 2005.
28. V. B. Mountcastle. The columnar organization of the neocortex. Brain, 120(4):701-22, 1997.
29. R. Nieuwenhuys, H. J. ten Donkelaar, and C. Nicholson. Section 22.11.6.6; Neocortex: Quantitative aspects and folding. In The Central Nervous System of Vertebrates, volume 3, pages 2008-2013. Springer-Verlag, Heidelberg, 1997.
30. A. Peters and E. G. Jones. Cerebral Cortex. Plenum Press, New York, 1984.
31. C. C. Petersen, T. T. Hahn, M. Mehta, A. Grinvald, and B. Sakmann. Interaction of sensory responses with spontaneous depolarization in layer 2/3 barrel cortex. PNAS, 100(23):13638-13643, 2003.
32. A. J. Rockel, R. W. Hirons, and T. P. S. Powell. Number of neurons through the full depth of the neocortex. Proc. Anat. Soc. Great Britain and Ireland, 118:371, 1974.
33. E. Ros, R. Carrillo, E. Ortigosa, B. Barbour, and R. Agís. Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics. Neural Comput., 18:2959-2993, 2006.
34. A. Schüz and G. Palm. Density of neurons and synapses in the cerebral cortex of the mouse. J. Comp. Neurol., 286:442-455, 1989.
35. A. J. Sherbondy, R. F. Dougherty, R. Ananthanaraynan, D. S. Modha, and B. A. Wandell. Think global, act local; projectome estimation with BlueMatter. In Proceedings of MICCAI 2009 Lecture Notes in Computer Science, pages 861-868, 2009.
36. S. Song, K. D. Miller, and L. F. Abbott. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neurosci., 3:919-926, 200