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    Goals

    The ultimate goal of the Blue Brain Project is to reverse engineer the mammalian brain. To achieve this goal theproject has set itself four key objectives:

    1. Create a Brain Simulation Facility with the ability to build models of the healthy and diseased brain, at different

    scales, with different levels of detail in different species

    2. Demonstrate the feasibility and value of this strategy by creating and validating a biologically detailed model of

    the neocortical column in the somatosensory cortex of young rats

    3. Use this model to discover basic principles governing the structure and function of the brain

    4. Exploit these principles to create larger more detailed brain models, and to develop strategies to model the

    complete human brain

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    Strategy

    The Blue Brain projects strategy hinges on two key elements.

    The first is the creation of a Brain Simulation Facility integrating the complete process of producing brain models

    from the acquisition of data from neuroscience experiments and the literature through the databasing and analysis of

    this data to model-building, simulation and the analysis and visualization of the results. This has required thedevelopment of detailed workflows and specialized software applications for every stage in the process. It has also

    required the creation and continuous updating of the necessary technological infrastructure: state-of-the art set-ups for

    the acquisition of experimental data and massive supercomputers for neuroinformatics, model building, simulation,

    data analysis and scientific visualization

    The second element in the strategy is the systematic search for basic principles of design that make it possible to

    predict specific features of the brain without measuring them directly. Examples include prediction of the distribution

    of ion channels from neurons electrical behavior and prediction of microcircuit connectivity from data on neuron

    morphology. This is what the project calls predictive reverse engineering.

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    Infrastructure

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    Main components of the infrastructure

    The Blue Brain workflow depends on a large-scale research infrastructure, providing:

    State of the art technology for the acquisition of data on different levels of brain organization (multi-patch clamp

    set-ups for studies of the electrophysiological behavior of neural circuits, Multielectrode Arrays MEAs allowing

    stimulation of and recording from brain slices, facilities for the creation and study of cell lines expressing

    particular ion channels, a variety of imaging systems, systems for the 3D reconstruction of neural morphologies);

    An IBM 16,384 core Blue Gene/P supercomputer for modeling and simulation (provided by CADMOS);

    A 32-processor SGI system, connected to the Blue Gene machine via dedicated 10Gbit/s fiber optic cables andproviding facilities for users to interact with visual representations of simulation results;

    A data center providing networked servers for use in data archiving and neuroinformatics.

    Data acquisition infrastructure

    The success of the Blue Brain project depends on very high volumes of standardized, high quality

    experimental data covering all possible levels of brain organization. Data comes both from the literature

    (via the projects automatic information extraction tools) and from experimental work conducted by the

    project itself. Blue Brains Data Acquisition Infrastructure provides the physical equipment necessary forthis work. Most of the experimental equipment is currently made available by the EPFL Laboratory of

    Neural Microcircuitry (LNMC). The planned Human Brain Project, if accepted, will massively increase the

    range of data sources.

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    High Performance Computing

    The Blue Brain workflow creates enormous demands for computational power. In Blue Brain cellular level

    models, the representation of the detailed electrophysioloy and communication of a single can require as

    many as 20,000 differential equations. No modern workstation is capable of solving this number of

    equations in biological real time. In other words, the only way for the project to achieve its goals is to use

    High Performance Computing (HPC).

    The Blue Brain projects simulation of the neocortical column incorporates detailed representations of

    10,000 neurons. A simulation of a whole brain rat model at the same level of detail would have to represent

    up to 100 million neurons and would require 20,000 times more memory. Simulating the human brain would

    require yet another 1,000-fold increase in memory and computational power. Subcellular modeling,

    modeling of the neuro-glial vascular system and the creation of virtual instruments (e.g. virtual EEG, virtual

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    In the initial phase of its work the Blue Brain project used an IBM BlueGene/L supercomputer with 8,192processors. Today, it uses a 16,384 core IBM BlueGene/P supercomputer with almost 8 times more

    memory than its predecessor. This machine is large enough to prototype mesoscale circuits containing up

    to several million neurons. Industry roadmaps suggest that exascale computers large enough to meet the

    projects requirements will be available by 2018-20. However, the transition to the new class of machines

    poses significant challenges.

    Constraints on energy consumption mean that supercomputers at the exascale and beyond will be less generic than

    current generation machines. If the new machines are to be useful for Blue Brain, the project will need to influence

    technology development. Key requirements include -Extremely large memory; -High memory bandwidth; -High IO

    capabilities; -In situ data analysis and visualization capabilities; -High-availability, high-uptime and interactivity.

    The design of the new machines will require novel hardware-software co-design strategies and novel tools

    supporting these strategies.

    Designers will need to support new modes of interaction between domain scientists and supercomputers, including

    realtime, interactive visualization, navigation and control of simulations and the use of the supercomputer as a

    virtual instrument; this will require a radical rethink of basic architectures.

    The Blue Brain Facility includes a powerful infrastructurefor High Performance Computing. including:

    A 4-rack IBM Blue Gene/P supercomputer for modeling and simulation;

    A 32-processor SGI system, providing facilities for users to interact with visual representations of simulation

    results

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    Publications

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    2011

    1. Hay E., Hill S., Schrmann F., Markram H, Segev I (2011). Models of Neocortical Layer 5b Pyramidal Cells

    Capturing a Wide Range of Dendritic and Perisomatic Active Properties. PLoS Computational Biology 7(7):

    e1002107. doi:10.1371/journal.pcbi.1002107

    2. Lasserre S., Hernando J., Hill S., Schuermann F., Anasagasti P.M., Jaoud, G.A., Markram H. (2011),A Neuron

    Membrane Mesh Representation for Visualization of Electrophysiological Simulations, IEEE Transactions onVisualization and Computer Graphics, 99 (preprints): p. 1-1.

    3. Shaul Druckmann, Thomas K, Berger. Felix Schrmann, Sean Hill, Henry Markram, ldan Segev

    (2011), Effective stimuli for constructing reliable neuron models,

    Plos Computational Biology, 7(8): e1002133. doi:10.1371/journal.pcbi.1002133

    4. Romand, S., Wang, Y., Toledo-Rodriguez, M., & Markram, H. (2011).Morphological development of thick-

    tufted layer V pyramidal cells in the rat somatosensory cortex. [Original Research]. Frontiers in Neuroanatomy,

    5.

    5. Anastassiou A.C., Perin R., Markram H. & Koch C., (2011), Ephaptic coupling of cortical neurons, Nature

    Neuroscience, 14:2 p 217

    6. Perin R., Berger T.K., & Markram H. (2011)A synaptic organizing principle for cortical neuronal groups, PNAS,

    108 (12)7. Hines M, Kumar S and Schrmann F (2011). Comparison of neuronal spike exchange methods on a Blue Gene/P

    supercomputer. Front. Comput. Neurosci. 5:49. doi: 10.3389/fncom.2011.00049

    8. Ramaswamy S, Hill SL, King JG, Schrmann F, Wang Y, Markram H (2011). Intrinsic Morphological Diversity

    of Thick-tufted Layer 5 Pyramidal Neurons Ensures Robust and Invariant Properties of in silico Synaptic

    Connections. J Physiol. 2011 Nov 14. [Epub ahead of print]

    9. Markram H, Gerstner W, Sjstrm PJ (2011). A history of spike-timing-dependent plasticity. Front Synaptic

    Neurosci. 2011;3:4. Epub 2011 Aug 29.

    10. Markram H, Perin R (2011). Innate neural assemblies for lego memory. Front Neural Circuits. 2011;5:6. Epub

    2011 May 16.

    2009

    1. King, J. G., Hines, M., Hill, S., Goodman, P. H., Markram, H., & Schurmann, F. (2009).A Component-Based

    Extension Framework for Large-Scale Parallel Simulations in NEURON. Front Neuroinformatics, 3, 10.

    2. Berger, T. K., Perin, R., Silberberg, G., & Markram, H. (2009).Frequency-dependent disynaptic inhibition in the

    pyramidal network: a ubiquitous pathway in the developing rat neocortex. J Physiol, 587(Pt 22), 5411-5425.

    2008

    1. Markram, H. (2008). Fixing the location and dimensions of functional neocortical columns.HFSP Journal, 2(3),

    132-135.

    2. Druckmann, S., Berger, T. K., Hill, S., Schurmann, F., Markram, H., & Segev, I. (2008).Evaluating automated

    parameter constraining procedures of neuron models by experimental and surrogate data. Biol Cybern, 99(4-5),

    371-379.

    3. Ascoli, G. A., Alonso-Nanclares, L., Anderson, S. A., Barrionuevo, G., Benavides-Piccione, R., Burkhalter, A., et

    al. (2008).Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat

    Rev Neurosci, 9(7), 557-568.

    4. Kndgen, H., Geisler, C., Fusi, S., Wang, X.-J., Lscher, H.-R., & Giugliano, M. (2008). The Dynamical

    Response Properties of Neocortical Neurons to Temporally Modulated Noisy Inputs In Vitro. Cerebral Cortex,

    18(9), 2086-2097.

    5. Hines, M. L., Eichner, H., & Schurmann, F. (2008).Neuron splitting in compute-bound parallel network

    simulations enables runtime scaling with twice as many processors. J Comput Neurosci, 25(1), 203-210.

    http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002107http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002107http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002107http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002107http://www.computer.org/portal/web/csdl/doi/10.1109/TVCG.2011.55http://www.computer.org/portal/web/csdl/doi/10.1109/TVCG.2011.55http://www.computer.org/portal/web/csdl/doi/10.1109/TVCG.2011.55http://www.computer.org/portal/web/csdl/doi/10.1109/TVCG.2011.55http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002133http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002133http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002133http://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2011%20-%20Morphological%20development%20of%20thick-tufted%20layer%20V%20pyramidal%20.PDFhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2011%20-%20Morphological%20development%20of%20thick-tufted%20layer%20V%20pyramidal%20.PDFhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2011%20-%20Morphological%20development%20of%20thick-tufted%20layer%20V%20pyramidal%20.PDFhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/perin%20papaer.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/perin%20papaer.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/perin%20papaer.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/perin%20papaer.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/PNAS-2011-Markram-1016051108.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/PNAS-2011-Markram-1016051108.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/PNAS-2011-Markram-1016051108.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_King_A%20component-based%20extension%20framework.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_King_A%20component-based%20extension%20framework.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_King_A%20component-based%20extension%20framework.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_King_A%20component-based%20extension%20framework.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_Berger_Frequency-Dependent.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_Berger_Frequency-Dependent.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_Berger_Frequency-Dependent.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_Berger_Frequency-Dependent.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Markram_Fixing%20the%20location%20and%20dimensions.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Markram_Fixing%20the%20location%20and%20dimensions.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Markram_Fixing%20the%20location%20and%20dimensions.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Druckmann_evaluating%20automated%20parameter.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Druckmann_evaluating%20automated%20parameter.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Druckmann_evaluating%20automated%20parameter.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_%20Ascoli_Petilla%20Terminology.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_%20Ascoli_Petilla%20Terminology.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20The%20dynamical%20response%20properties%20of%20neocortical%20neurons%20to%20temporally%20modulated%20noisy%20inputs%20in%20vitro.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20The%20dynamical%20response%20properties%20of%20neocortical%20neurons%20to%20temporally%20modulated%20noisy%20inputs%20in%20vitro.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20The%20dynamical%20response%20properties%20of%20neocortical%20neurons%20to%20temporally%20modulated%20noisy%20inputs%20in%20vitro.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20The%20dynamical%20response%20properties%20of%20neocortical%20neurons%20to%20temporally%20modulated%20noisy%20inputs%20in%20vitro.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Neuron%20splitting%20in%20compute-bound%20parallel%20network.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Neuron%20splitting%20in%20compute-bound%20parallel%20network.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Neuron%20splitting%20in%20compute-bound%20parallel%20network.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Neuron%20splitting%20in%20compute-bound%20parallel%20network.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Neuron%20splitting%20in%20compute-bound%20parallel%20network.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Neuron%20splitting%20in%20compute-bound%20parallel%20network.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20The%20dynamical%20response%20properties%20of%20neocortical%20neurons%20to%20temporally%20modulated%20noisy%20inputs%20in%20vitro.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20The%20dynamical%20response%20properties%20of%20neocortical%20neurons%20to%20temporally%20modulated%20noisy%20inputs%20in%20vitro.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_%20Ascoli_Petilla%20Terminology.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Druckmann_evaluating%20automated%20parameter.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Druckmann_evaluating%20automated%20parameter.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008_Markram_Fixing%20the%20location%20and%20dimensions.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_Berger_Frequency-Dependent.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_Berger_Frequency-Dependent.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_King_A%20component-based%20extension%20framework.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2009_King_A%20component-based%20extension%20framework.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/PNAS-2011-Markram-1016051108.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/perin%20papaer.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/perin%20papaer.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2011%20-%20Morphological%20development%20of%20thick-tufted%20layer%20V%20pyramidal%20.PDFhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2011%20-%20Morphological%20development%20of%20thick-tufted%20layer%20V%20pyramidal%20.PDFhttp://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002133http://www.computer.org/portal/web/csdl/doi/10.1109/TVCG.2011.55http://www.computer.org/portal/web/csdl/doi/10.1109/TVCG.2011.55http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002107http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002107

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    6. Cal, C., Berger, T., Pignatelli, M., Carleton, A., Markram, H., & Giugliano, M. (2008). Inferring connection

    proximity in networks of electrically coupled cells by subthreshold frequency response analysis.Journal of

    Computational Neuroscience, 24(3), 330-345.

    7. Hines, M. L., Markram, H., & Schurmann, F. (2008).Fully implicit parallel simulation of single neurons. J

    Comput Neurosci, 25(3), 439-448.

    8. Kozloski, J., Sfyrakis, K., Hill, S., Schurmann, F., Peck, C., & Markram, H. (2008).Identifying, Tabulating, and

    Analyzing Contacts between Branched Neuron Morphologies. IBM Journal of Research and

    Development, 52(1/2), 43-55.

    2007

    1. Arsiero, M., Lscher, H.-R., Lundstrom, B. N., & Giugliano, M. (2007).The Impact of Input Fluctuations on the

    FrequencyCurrent Relationships of Layer 5 Pyramidal Neurons in the Rat Medial Prefrontal Cortex. The Journal

    of Neuroscience, 27(12), 3274-3284.

    2. Le Be, J. V., Silberberg, G., Wang, Y., & Markram, H. (2007). Morphological, electrophysiological, and synapticproperties of corticocallosal pyramidal cells in the neonatal rat neocortex.Cereb Cortex, 17(9), 2204-2213.

    3. Silberberg, G., & Markram, H. (2007).Disynaptic inhibition between neocortical pyramidal cells mediated by

    Martinotti cells.Neuron, 53(5), 735-746.

    4. Markram, H. (2007).Bioinformatics: industrializing neuroscience. Nature, 445(7124), 160-161.

    5. Druckmann, S., Banitt, Y., Gidon, A., Schurmann, F., Markram, H., & Segev, I. (2007). A novel multiple

    objective optimization framework for constraining conductance-based neuron models by experimental data. Front

    Neurosci, 1(1), 7-18.

    2006

    1. Berger, T., Lscher, H. R., & Giugliano, M. (2006).Transient rhythmic network activity in the somatosensory

    cortex evoked by distributed input in vitro. [doi: DOI: 10.1016/j.neuroscience.2006.03.003]. Neuroscience,

    140(4), 1401-1413.

    2. Markram, H. (2006).The Blue Brain Project. Nature Reviews Neuroscience, 7(February 2006), 153-160.

    3. Le B, J.-V., & Markram, H. (2006).Spontaneous and evoked synaptic rewiring in the neonatal neocortex.

    Proceedings of the National Academy of Sciences, 103(35), 13214-13219.

    4. Migliore, M., Cannia, C., Lytton, W. W., Markram, H., & Hines, M. L. (2006). Parallel network simulations with

    NEURON. J Comput Neurosci, 21(2), 119-129.

    5. Wang, Y., Markram, H., Goodman, P. H., Berger, T. K., Ma, J., & Goldman-Rakic, P. S. (2006).Heterogeneity in

    the pyramidal network of the medial prefrontal cortex.[10.1038/nn1670]. Nat Neurosci, 9(4), 534-542.

    6. Le Be, J. V., & markram, H. (2006). [A new mechanism for memory: neuronal networks rewiring in the young

    rat neocortex]. Med Sci (Paris), 22(12), 1031-1033.

    2005

    1. Muhammad, A. J., & Markram, H. (2005).NEOBASE: databasing the neocortical microcircuit. Stud Health

    Technol Inform, 112, 167-177.

    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    The Human Brain Project

    Blue Brains success in modeling the rat cortical column has driven the development of the Brain Simulation Facility

    and has demonstrated the feasibility of the projects general strategy. But, this is only a first step.

    The human brain is an immensely powerful, energy efficient, self-learning, self-repairing computer. If we could

    understand and mimic the way it works, we could revolutionize information technology, medicine and society. To do

    so we have to bring together everything we know and everything we can learn about the inner workings of the brain's

    molecules, cells and circuits. With this goal in mind, the Blue Brain team has recently come together with 12 other

    European and international partners to propose the Human Brain Project (HBP), a candidate for funding under the

    EUs FET Flagship program. The HBP team will include many of Europes best neuroscientists, doctors, physicists,

    mathematicians, computer engineers and ethicists. The goal is to build on the work of the Blue Brain Project and on

    work by the other partners to integrate everything we know about the brain in massive databases and in detailed

    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    computer models. This will require breakthroughs in mathematics and software engineering, an international

    supercomputing facility more powerful than any before and a strong sense of social responsibility.

    Experimental and clinical data is accumulating exponentially. Computers powerful enough to meet the projects initial

    requirements are already here. As technology progresses and the project discovers new principles of brain design, it

    will build ever more realistic models. The benefits for society will be huge, even before it achieves its final goals. The

    HBPs thirst for computing power will drive the development of new technologies for supercomputing and forscientific visualization. Models of the brain will revolutionize information technology, allowing us to design

    computers, robots, sensors and other devices far more powerful, more intelligent and more energy efficient than any

    we know today. Brain simulation will help us understand the root causes of brain diseases, to diagnose them early, to

    develop new treatments, and to reduce reliance on animal testing. The project will also throw new light on questions

    human beings have been asking for more than two and a half thousand years. What does it mean to perceive, to think,

    to remember, to learn, to know, to decide? What does it mean to be conscious? In summary, the Human Brain Project

    has the potential to revolutionize technology, medicine, neuroscience, and society.

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    Glossary

    Term Description

    -omics The study of the humane genome gave rise to the

    science and technology of genomics the study of

    the genome. Other omics studies and

    technologies refer to other, higher levels of

    biological organization e.g. transcriptomics (the

    study of mRNA produced when genes are

    transcribed; proteomics (the study of the proteinsproduced when mRNA is translated). When

    omics is used as a general term, it refers to the

    complete set of all such studies and technologies

    covering many levels of biological information.

    Atlas A work of reference (e.g. the Allen Mouse Atlas)

    showing how one or more data sets (e.g. gene

    expression data) map to specific regions and

    subregions of the brain.

    BG/P See Blue Gene/P.

    BlueGene/P IBM supercomputer. The BlueGene/P used in the

    Blue Brain project is a massively parallel, tightly

    interconnect machine with 16384 processors,

    56TeraFlops of peak performance, 16TeraByte of

    distributed memory and a 1 PetaByte file system.

    Bouton An enlarged part of the axon of a nerve cells

    forming the presynaptic terminal of a synapse with

    another cell.

    Builder A Blue Brain software application used to buildmodels at a specific level of brain organization.

    c-code Standardized Blue Brain characterization of the

    electrical behavior of a neural microcircuit,

    obtained through application of a benchmark

    stimulation protocol.

    Cable equation A mathematical formulation, derived from

    classical electrodynamics, describing the

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    propagation of current and voltage along a cable or

    (in the case of neuroscience) a neural fiber.

    Capability job A class of compute jobs that requires exclusive

    access to very large, tightly-coupled

    supercomputers. Simulation-based virtual

    experiments belong to this class.

    Capacity job A class of compute jobs that requires medium sizesupercomputing resources. Numerous preparatory

    steps in the Blue Brain facility (e.g. builders)

    involve this kind of job.

    Channelome The full set of ion channels expressed by a cell.

    Class hierarchy Computer science term describing a hierarchy of

    classes at increasing levels of generality (e.g.

    individual, species, genus, family, etc.). Any

    object is an instance of a class in the hierarchy

    and of the superclasses to which the class belongs.

    Compartmentalized model A spatially discretized model of a neuron in which

    the neuron is represented by a set of digitizedcompartments each with its own individual

    attributes. Used for purposes of numerical

    approximation.

    Connectome The complete connectivity map between neurons,

    including the locations of all synapses.

    Connectomics The study of the connectome.

    Curation Human processing (quality control, normalization

    of terminology, annotation etc.) of data prior to use

    in modeling.

    Data Model An abstract model of the different classes of data

    used by a computer application or system and the

    hierarchical relationships between these classes.Diffusion Tensor Imaging (DTI) A magnetic resonance imaging (MRI) technique

    used to produce images of neural tracts.

    DTI See Diffusion Tensor Imaging.

    e-code Standardized Blue Brain characterization of the

    electrical behavior of a cell, obtained through

    application of a benchmark stimulation protocol.

    Electron Microscopy (EM) Use of an Electron Microscope to obtain highly

    magnified images of biological tissues.

    EM See Electron Microscopy.

    Endoplasmic Reticulum (ER). An extensive system of parallel and folded

    membranes within a neuron.

    Ephaptic effects Effects due to current flow through theextracellular space.

    Field Potential An electrical potential created by a set of current

    sources.

    Genetic Algorithm An optimization technique based on a highly

    stylized version of Darwinian evolution.

    High Performance Computing (HPC) The use of parallel processing to run advanced

    applications programs efficiently, reliably and

    quickly. The term HPC is sometimes used as a

    synonym for supercomputing, although technically

    a supercomputer is a system that performs at or

    near the currently highest operational rate for

    computers.

    Hodgkin-Huxley channel models Phenomenological description of genetically

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    prescribed ion channels.

    HPC See High Performance Computing.

    Ion channels Proteins controlling the passage of ions through the

    cell membrane. Ion channels are targets for

    neuromodulatory systems and for drugs. The

    distribution of ion channels determines the

    electrical behavior of the cell.

    macrocircuit Circuit linking different regions of the brain.

    Marching cube space fill Algorithm used to extract a surface mesh from a

    volume representation for parametrized geometry.

    MCell A widely used simulator from the Computational

    Neurobiology Lab, SALK Institute, USA. Mcell is

    used in reaction diffusion simulations of molecular

    interactions.

    MEA See Multi-Electrode Array.

    Mesh representation A term from 3D computer graphics. A

    representation based on a polygon mesh or an

    unstructured grid: a collection of vertices, edgesand faces that defines the shape of a polyhedron.

    Mesocircuit Circuit linking multiple microcircuits.

    Microcircuit A neural circuit lying within the dimensions of the

    local arborizations of neurons (typically 200

    500 m).

    Morph Adapt a template model to a new set of parameters

    indicated by experiments or defined by a

    researcher.

    Multi-Electrode ARRAY (MEA) An array of electrodes allowing simultaneous

    stimulation of and recording from neural tissue.

    Multiomics The study of a system using multiple

    -omic levels of organization.Multi-objective Optimization An optimization procedure that measure the

    fitness of alternative solutions in terms of more

    than one objective.

    Neocortical column A basic functional unit of the neocortex organized

    as a densely interconnected column of neurons

    traversing all 6 layers.

    NEURON A well known environment for the empirically-

    based simulations of neurons and networks of

    neurons. Developed by Michael Hines, Yale

    University, USA.

    Out of core Computer memory outside the core of the

    machine (e.g. the memory used to store the contentof a database). The use of out of core memory

    makes it possible to perform computations on data

    sets too large to fit into core memory.

    Patch clamp A widely used technique for simultaneously

    stimulating and recording from neurons. The Blue

    Brain Project has pioneered the use of patch clamp

    techniques with as many as 12 neurons.

    Phenome The complete set of phenotypic entities

    (morphology, behavior etc.) expressed by a cell, a

    tissue, an organ, an organism or a species.

    Phenomenological model A model that reproduces an observed behavior

    without faithfully accounting for the underlying

    biophysics.

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    Predictive disease diagnostics Techniques making it possible to predict the risk

    that a human subject will develop a disease.

    Predictive reverse engineering Techniques making it possible to predict unknown

    data from a small subset of the data, or from data

    at other levels of biological organization or from

    other species.

    Probabilistic Synapse Model Extension of the Tsyodyks-Markram Synapse

    Model for probabilistic release.

    Proteome The set of information required to fully represent

    the proteins expressed by a cell

    Receptome The set of information required to fully represent

    the receptors expressed by a cell.

    Reconstruction Technique used to trace and digitize the 3D

    morphology of a nerve cell from stained tissue

    through 2D microscopy.

    Registration The process whereby a concept (e.g. the name of a

    brain region) or a set of experimental data (e.g.

    data describing a brain region in an individual) ismapped to coordinates in an atlas.

    Repair (of morphologies) Correction of 3D neuron morphologies to remove

    artifacts (typically slicing artifacts and artifacts due

    to tissue shrinkage).

    Representation A structured set of data representing an underlying

    physical reality (e.g. the morphology of a neuron).

    SBML See Systems Biology Markup Language.

    STEPS A simulator for stochastic reaction-diffusion

    systems in realistic morphologies from the

    Theoretical Neurobiology group, University of

    Antwerp, Belgium.

    Systems Biology Markup Language (SBML) A computer-readable format for representingmodels of biological processes.

    Template model A generic model representing the structure and/or

    the function of the brain or a part of the brain at a

    specific level of detail. In the Blue Brain strategy,

    template models can be morphed to match

    different parameter sets coming from experiments

    or defined by a researcher e.g. morphing of data

    from one region of the brain to match the features

    of another region; morphing of data from one

    species to characteristics of a different species.

    Touch A structural contact. The point where the axon of

    one neuron comes within a threshold distance ofpart of another neuron (usually a dendrite).

    Touch region The region surrounding a touch.

    Tract tracing The use of a labeling agent to detect long-range

    pathways across the brain.

    Transcriptome The set of information required to fully represent

    all mRNA expressed by a cell during transcription

    of the genome.

    Tsyodyks-Markram Synapse Model Phenomenological description of major synapse

    classes and their short-term adaptation.

    Ultrastructure The fine (microscopic) structure of a cell.

    Volume Representation Voxelized representation of a 3D space, typically a

    regular three-dimensional matrix.

    Voxel Term from computer graphics. A volumetric pixel

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    or, more correctly, a Volumetric Picture

    representing a value on a grid in 3D space.

    Analogous to a pixel in 2D images.

    Work flow Term used both in management engineering and in

    computer science. A sequence of steps leading to a

    well-defined outcome.

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    In briefReconstructing the brain piece by piece and building a virtual brain in a supercomputerthese are some of the goals

    of the Blue Brain Project. The virtual brain will be an exceptional tool giving neuroscientists a new understanding

    of the brain and a better understanding of neurological diseases.

    The Blue Brain project began in 2005 with an agreement between the EPFL and IBM, which supplied theBlueGene/L supercomputer acquired by EPFL to build the virtual brain.

    The computing power needed is considerable. Each simulated neuron requires the equivalent of a laptop

    computer. A model of the whole brain would have billions. Supercomputing technology is rapidly approaching a

    level where simulating the whole brain becomes a concrete possibility.

    As a first step, the project succeeded in simulating a rat cortical column. This neuronal network, the size of a

    pinhead, recurs repeatedly in the cortex. A rats brain has about 100,000 columns of in the order of 10,000 neurons

    each. In humans, the numbers are dizzyinga human cortex may have as many as two million columns, each having

    in the order of 100,000 neurons each.

    Blue Brain is a resounding success. In five years of work, Henry Markrams team has perfected a facility that can

    create realistic models of one of the brains essential building blocks. This process is entirely data driven and

    essentially automatically executed on the supercomputer. Meanwhile the generated models show a behavior already

    observed in years of neuroscientific experiments. These models will be basic building blocks for larger scale models

    leading towards a complete virtual brain.

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    A tool for researchers

    The brains extreme complexity makes it one of the most difficult subjects to study. Forexample, it is totally impossible to

    observe what is happening within a small group of neurons while at the same time imaging the activity of the whole brain. A

    virtual model would make such observations possible.

    In terms ofpublic health, the stakes are high. A realistic simulation could provide a better understanding of the way drugsact on the brain, and of their possible side effects. It could even help to develop completely new treatments.

    Today, every new drug put on the market costs an average of 1.3 billion francs to develop. Neurological diseases are

    destined to represent an ever-increasing share of health-care budgets, and are a source of considerable suffering for those

    afflicted and their family and friends.

    The Blue Brain project sets out to make neuroscientific research more efficient and in the long run will help to limitthe need to use laboratory animals.

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    What do we simulate?The Blue Brain project represents an essential first step toward achieving a complete virtual human brain. The researchers

    have demonstrated the validity of their method by developing a realistic model of a rat cortical column, consisting of about

    10,000 neurons. Eventually, of course, the goal is to simulate systems of millions and hundreds of millions of neurons.

    Understand the Cortical ColumnThe cortical column can be considered the basic unit of the cortex. Notably, it is by accumulating an ever-increasing number

    of columns that the brain has evolved over millions of years. Each column seems to be allotted to a simple yet essential

    function. For example, it has been possible to show that in the rat, one specific column is devoted to each whisker.

    The cortical column is no larger than the head of a pin. In the rat, it contains only about 10,000 neurons. But as a basic unit,

    it represents an essential component of cerebral mechanics. That is why, initially, the researchers are working to simulate its

    functioning.

    The Blue Brain project team has succeeded in isolating about fifty different types of neuron within the cortical column. As in

    an ecosystem, each species differs from the others in essential characteristics such as morphology, behavior, population

    density, etc.

    Move From the Real to the VirtualThe researchers have been working to explain the behavior of and the way they connect to form circuits. This kind of

    knowledge makes it possible to isolate basic principles they can incorporate in their simulations.

    The scientists have translated their observations into mathematics, developing powerful algorithms to represent neuronal

    behavior in a realistic way, and to make the best possible use of supercomputing power.

    Test the ModelEach two weeks, on average, the BlueGene/P supercomputer generates and runs a new model of the cortical column,

    incorporating the latest data from experiments. Often these simulations reproduce experiments that have already been

    performed with living neurons. For example, they may stimulate a virtual neuron and observe the way it reacts. The

    scientists do not intervene in the model to affect the results. In this way, they can truly test the general principles behind themodel. The results so far show that the models are already achieving a high level of realism.

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    What's next?The ultimate goals of brain simulation are to answer age-old questions about how we think, remember, learn and feel,

    to discover new treatments for the scourge of brain disease and to build new computer technologies that exploit what

    we have learned about the brain.

    Blue Brain is a first step in this direction. But we need to go further.

    This is why Blue Brain recently joined with other 12 partners to propose the Human Brain Projecta very large 10year project that will pursue precisely these aims. The new grouping has just been awarded a Eur 1.4 million

    European grant to formulate a detailed proposal.

    The EU decision to launch the project is expected in 2012.

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    Timeline

    2002

    http://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://window.print%28%29/http://window.print%28%29/http://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://window.print%28%29/http://window.print%28%29/http://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://window.print%28%29/http://window.print%28%29/http://window.print%28%29/http://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://window.print%28%29/http://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://window.print%28%29/http://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7ahttp://www.addthis.com/bookmark.php?v=250&username=xa-4c1a9d5b3161cf7a

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    Henry Markram founds the Brain Mind Institute (BMI)at the EPFL.

    2005On June 6, the EPFL and IBM sign an agreement to launch the Blue Brain project. The agreement provides for the

    installation of aBlueGenesupercomputer on campus.

    2006In February, the project takes shape and Henry Markram publishes an article about the Blue Brain project inNature Reviews

    Neuroscience.

    During the summer, the first cortical column model of 10,000 neurons is created using a simplified neuronal model.

    In December, an auto-generated cortical column is completed. It is a biologically valid model, from a neuronal standpoint.

    2007In January, the project is presented to the Davos forum.

    On November 26, the end of the first phasethe modeling and simulation of the first rat cortical columnis announced (a

    link to a picture would be good).

    2008Electrophysical, anatomical and genetic laboratory experiments are used to test the data-driven auto-generation process for

    neurons.

    In June, an article on determining the position and size of functional cortical columns is published in theHFSP Journal.

    2009In June, the BlueGene/L supercomputer is replaced by BlueGene/P, increasing computing power by doubling the number of

    processors.

    "In silico"experimentation is in full swing, testing the protocols published by other research groups and adding toknowledge of the principles governing cortical column construction.

    2010An article entitled An Approach to Capturing Neuron Morphological Diversity is published by MIT press inComputational

    Modeling Methods for Neuroscientists.

    In December, with a large group of partners, the Blue Brain Project applies to the European Commission(Seventh

    Framework Programme - FP7) for financing for a larger project aiming to continue the work and simulate an entire brain,

    specifically a human brain (find out what we can say HBSP).

    GlossaryAxon: A fine extension of neurons, which makes them the longest cells in the human body and conducts electrical signalsvery efficiently thanks to a special sheath surrounding it.

    Ion channel: A group of specific molecules located in cell membranes. The molecules special properties allow or preventthe passage of certain substances between the inside and outside of the cell. This process works in many different ways

    depending on the molecules electrochemical properties.

    Cortical column: A group of neuronsabout 10,000 in rats and 70,000 in humansthat is vertically structured in relation

    to the various levels of the cerebral cortex and constitutes a unit associated with a specific function.

    Cortex: A region of the brain consisting of several areas associated with specific complex cognitive processes. It is

    structured as different layers of neurons and other brain cells.

    http://bmi.epfl.ch/http://bmi.epfl.ch/http://bmi.epfl.ch/http://www-03.ibm.com/systems/deepcomputing/solutions/bluegene/http://www-03.ibm.com/systems/deepcomputing/solutions/bluegene/http://www-03.ibm.com/systems/deepcomputing/solutions/bluegene/http://www.nature.com/nrn/journal/v7/n2/full/nrn1848.htmlhttp://www.nature.com/nrn/journal/v7/n2/full/nrn1848.htmlhttp://www.nature.com/nrn/journal/v7/n2/full/nrn1848.htmlhttp://www.nature.com/nrn/journal/v7/n2/full/nrn1848.htmlhttp://hebb.mit.edu/courses/connectomics/Markram%20location%20dimensions%20functional%20neocortical%20columns%2008.pdfhttp://hebb.mit.edu/courses/connectomics/Markram%20location%20dimensions%20functional%20neocortical%20columns%2008.pdfhttp://hebb.mit.edu/courses/connectomics/Markram%20location%20dimensions%20functional%20neocortical%20columns%2008.pdfhttp://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11988http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11988http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11988http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11988http://cordis.europa.eu/fp7/home_en.htmlhttp://cordis.europa.eu/fp7/home_en.htmlhttp://cordis.europa.eu/fp7/home_en.htmlhttp://cordis.europa.eu/fp7/home_en.htmlhttp://cordis.europa.eu/fp7/home_en.htmlhttp://cordis.europa.eu/fp7/home_en.htmlhttp://cordis.europa.eu/fp7/home_en.htmlhttp://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11988http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11988http://hebb.mit.edu/courses/connectomics/Markram%20location%20dimensions%20functional%20neocortical%20columns%2008.pdfhttp://www.nature.com/nrn/journal/v7/n2/full/nrn1848.htmlhttp://www.nature.com/nrn/journal/v7/n2/full/nrn1848.htmlhttp://www-03.ibm.com/systems/deepcomputing/solutions/bluegene/http://bmi.epfl.ch/http://bmi.epfl.ch/

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    Dendrite: An extension of the neurons cell body, ending in synapses; dendrites conduct electrical currents from thesynapse toward the center of the cell.

    Neuroinformatics: A branch of science covering the organization of neuroscience data and the development of analyticalmodels and tools. Not to be confused with neuromorphic computing, which seeks to reproduce cerebral mechanisms for

    useful purposes.

    Neuron: A cell of the nervous system that can transmit and receive bioelectrical signals (the nerve impulse). Its star-likeshape allows it to interconnect with other neurons.

    Neurotransmitters: Chemical compounds released into the synapses by neurons. They make communication between theneurons possible. They can also facilitate or inhibit signal transmission.

    Synapse: The area of contact between nerve cells that enables them to exchange electrical or chemical signals and permits

    communication between them


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