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⇱ The Blue Brain Project | Nature Reviews Neuroscience

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Abstract

IBM's Blue Gene supercomputer allows a quantum leap in the level of detail at which the brain can be modelled. I argue that the time is right to begin assimilating the wealth of data that has been accumulated over the past century and start building biologically accurate models of the brain from first principles to aid our understanding of brain function and dysfunction.

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Figure 1: The Blue Gene/L supercomputer architecture.
Figure 2: Elementary building blocks of neural microcircuits.
Figure 3: Reconstructing the neocortical column.
Figure 4: The data manipulation cascade.

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References

  1. The Blue Brain Project [online], <http://bluebrainproject.epfl.ch> (2005).

  2. Blue Gene [online], <http://www.research.ibm.com/bluegene> (2005).

  3. Deep Blue [online], <http://www.research.ibm.com/deepblue> (2005).

  4. Hodgkin, A. L. & Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. (Lond.) 117, 500–544 (1952).

    CAS  Google Scholar 

  5. Rall, W. Branching dendritic trees and motoneuron membrane resistivity. Exp. Neurol. 1, 491–527 (1959).

    Article  CAS  Google Scholar 

  6. Segev, I. & Rall, W. Excitable dendrites and spines: earlier theoretical insights elucidate recent direct observations. Trends Neurosci. 21, 453–460 (1998).

    Article  CAS  Google Scholar 

  7. Johnston, D. et al. Active dendrites, potassium channels and synaptic plasticity. Phil. Trans. R. Soc. Lond. B 358, 667–674 (2003).

    Article  CAS  Google Scholar 

  8. Magee, J. C. Dendritic integration of excitatory synaptic input. Nature Rev. Neurosci. 1, 181–190 (2000).

    Article  CAS  Google Scholar 

  9. London, M. & Hausser, M. Dendritic computation. Annu. Rev. Neurosci. 28, 503–532 (2005).

    Article  CAS  Google Scholar 

  10. Migliore, M. & Shepherd, G. M. Emerging rules for the distributions of active dendritic conductances. Nature Rev. Neurosci. 3, 362–370 (2002).

    Article  CAS  Google Scholar 

  11. Rall, W. & Shepherd, G. M. Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. J. Neurophysiol. 31, 884–915 (1968).

    Article  CAS  Google Scholar 

  12. Pellionisz, A., Llinas, R. & Perkel, D. H. A computer model of the cerebellar cortex of the frog. Neuroscience 2, 19–35 (1977).

    Article  CAS  Google Scholar 

  13. Shepherd, G. M. & Brayton, R. K. Computer simulation of a dendrodendritic synaptic circuit for self- and lateral-inhibition in the olfactory bulb. Brain Res. 175, 377–382 (1979).

    Article  CAS  Google Scholar 

  14. Traub, R. D. & Wong, R. K. Cellular mechanism of neuronal synchronization in epilepsy. Science 216, 745–747 (1982).

    Article  CAS  Google Scholar 

  15. Traub, R. D., Miles, R. & Buzsaki, G. Computer simulation of carbachol-driven rhythmic population oscillations in the CA3 region of the in vitro rat hippocampus. J. Physiol. (Lond.) 451, 653–672 (1992).

    Article  CAS  Google Scholar 

  16. Douglas, R. J. & Martin, K. A. C. A functional microcircuit for cat visual cortex. J. Physiol. (Lond.) 440, 735–769 (1991).

    Article  CAS  Google Scholar 

  17. Wang, X. J. & Rinzel, J. Spindle rhythmicity in the reticularis thalami nucleus: synchronization among mutually inhibitory neurons. Neuroscience 53, 899–904 (1993).

    Article  CAS  Google Scholar 

  18. De Schutter, E. & Bower, J. M. An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice. J. Neurophysiol. 71, 375–400 (1994).

    Article  CAS  Google Scholar 

  19. Bush, P. & Sejnowski, T. J. Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models. J. Comput. Neurosci. 3, 91–110 (1996).

    Article  CAS  Google Scholar 

  20. Contreras, D., Destexhe, A., Sejnowski, T. J. & Steriade, M. Control of spatiotemporal coherence of a thalamic oscillation by corticothalamic feedback. Science 274, 771–774 (1996).

    Article  CAS  Google Scholar 

  21. Destexhe, A., Bal, T., McCormick, D. A. & Sejnowski, T. J. Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices. J. Neurophysiol. 76, 2049–2070 (1996).

    Article  CAS  Google Scholar 

  22. Golomb, D. & Amitai, Y. Propagating neuronal discharges in neocortical slices: computational and experimental study. J. Neurophysiol. 78, 1199–1211 (1997).

    Article  CAS  Google Scholar 

  23. Lytton, W. W., Contreras, D., Destexhe, A. & Steriade, M. Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures. J. Neurophysiol. 77, 1679–1696 (1997).

    Article  CAS  Google Scholar 

  24. Destexhe, A., Contreras, D. & Steriade, M. Cortically-induced coherence of a thalamic-generated oscillation. Neuroscience 92, 427–443 (1999).

    Article  CAS  Google Scholar 

  25. Egger, V., Feldmeyer, D. & Sakmann, B. Coincidence detection and changes of synaptic efficacy in spiny stellate neurons in rat barrel cortex. Nature Neurosci. 2, 1098–1105 (1999).

    Article  CAS  Google Scholar 

  26. Bal, T., Debay, D. & Destexhe, A. Cortical feedback controls the frequency and synchrony of oscillations in the visual thalamus. J. Neurosci. 20, 7478–7488 (2000).

    Article  CAS  Google Scholar 

  27. Howell, F. W., Dyrhfjeld-Johnsen, J., Maex, R., Goddard, N. & De Schutter, E. A large scale model of the cerebellar cortex using PGENESIS. Neurocomputing 32, 1036–1041 (2000).

    Google Scholar 

  28. Kozlov, A., Kotaleski, J. H., Aurell, E., Grillner, S. & Lansner, A. Modeling of substance P and 5-HT induced synaptic plasticity in the lamprey spinal CPG: consequences for network pattern generation. J. Comput. Neurosci. 11, 183–200 (2001).

    Article  CAS  Google Scholar 

  29. Bazhenov, M., Timofeev, I., Steriade, M. & Sejnowski, T. J. Model of thalamocortical slow-wave sleep oscillations and transitions to activated states. J. Neurosci. 22, 8691–8704 (2002).

    Article  CAS  Google Scholar 

  30. Pinto, D. J., Jones, S. R., Kaper, T. J. & Kopell, N. Analysis of state-dependent transitions in frequency and long-distance coordination in a model oscillatory cortical circuit. J. Comput. Neurosci. 15, 283–298 (2003).

    Article  Google Scholar 

  31. Bazhenov, M., Timofeev, I., Steriade, M. & Sejnowski, T. J. Potassium model for slow (2–3 Hz) in vivo neocortical paroxysmal oscillations. J. Neurophysiol. 92, 1116–1132 (2004).

    Article  CAS  Google Scholar 

  32. Traub, R. D. et al. Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J. Neurophysiol. 93, 2194–2232 (2005).

    Article  Google Scholar 

  33. Thomson, A. M., Girdlestone, D. & West, D. C. Voltage-dependent currents prolong single-axon postsynaptic potentials in layer III pyramidal neurons in rat neocortical slices. J. Neurophysiol. 60, 1896–1907 (1988).

    Article  CAS  Google Scholar 

  34. Dodt, H. U. & Zieglgansberger, W. Visualizing unstained neurons in living brain slices by infrared DIC-videomicroscopy. Brain Res. 537, 333–336 (1990).

    Article  CAS  Google Scholar 

  35. Stuart, G. J., Dodt, H. U. & Sakmann, B. Patch-clamp recordings from the soma and dendrites of neurons in brain slices using infrared video microscopy. Pflugers Arch. 423, 511–518 (1993).

    Article  CAS  Google Scholar 

  36. Markram, H. et al. Interneurons of the neocortical inhibitory system. Nature Rev. Neurosci. 5, 793–807 (2004).

    CAS  Google Scholar 

  37. Martin, K. A. Microcircuits in visual cortex. Curr. Opin. Neurobiol. 12, 418–425 (2002).

    Article  CAS  Google Scholar 

  38. Silberberg, G., Gupta, A. & Markram, H. Stereotypy in neocortical microcircuits. Trends Neurosci. 25, 227–230 (2002).

    Article  CAS  Google Scholar 

  39. Toledo-Rodriguez, M. et al. Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cereb. Cortex 14, 1310–1327 (2004).

    Article  Google Scholar 

  40. NEURON [online], <http://www.neuron.yale.edu/neuron> (2005).

  41. Tsodyks, M., Pawleslik, K. & Markram, H. Neural networks with dynamic synapses. Neural Comput. 10, 821–835 (1998).

    Article  CAS  Google Scholar 

  42. NeoCortical Simulator [online], <http://brain.cse.unr.edu/ncsDocs> (2005).

  43. SGI [online], <http://www.SGI.com> (2005).

  44. The Human Brain Project [online], <http://www.nimh.nih.gov/neuroinformatics> (2005).

  45. Markram, H. Dendritic object theory: a theory of the neural code where 3D electrical objects are formed across dendrites by neural microcircuits. Swiss Soc. Neurosci. Abstr. 196 (2005).

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Acknowledgements

I am grateful for the efforts of all my students, especially Y. Wang, A. Gupta, M. Toledo and G. Silberberg, in carrying out such challenging experiments and producing such incredible data. I thank P. Aebischer, G. Margaritondo, F. Avellan, G. Parisod and the entire EPFL (Ecole Polytechnique Fédérale de Lausanne) administration for their support of this project and for acquiring Blue Gene. I thank IBM (International Business Machines) for making this prototype supercomputer available and for their major support of neuroscience. I also thank SGI (Silicon Graphics, Inc.) for their major initiative to help with the visualization of the Blue Brain. I thank P. Goodman for his long-standing support of our reconstruction efforts and for introducing me to the Blue Gene initiative in 2000. Thanks also to the US Office of Naval Research for their support. I thank I. Segev, who is and will be essential to the success of the project, and G. Shepherd for their valuable comments on the manuscript.

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  1. the Laboratory of Neural Microcircuitry, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland

    Henry Markram

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  1. Henry Markram

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Markram, H. The Blue Brain Project. Nat Rev Neurosci 7, 153–160 (2006). https://doi.org/10.1038/nrn1848

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