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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.
http://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Inferring%20connection%20proximity%20in%20networks%20of%20electrically%20coupled%20cells.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Inferring%20connection%20proximity%20in%20networks%20of%20electrically%20coupled%20cells.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Inferring%20connection%20proximity%20in%20networks%20of%20electrically%20coupled%20cells.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Inferring%20connection%20proximity%20in%20networks%20of%20electrically%20coupled%20cells.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Fully%20Implicit%20Parallel%20Simulation%20of%20Single%20Neurons.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Fully%20Implicit%20Parallel%20Simulation%20of%20Single%20Neurons.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Fully%20Implicit%20Parallel%20Simulation%20of%20Single%20Neurons.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Fully%20Implicit%20Parallel%20Simulation%20of%20Single%20Neurons.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20%20Identifying,%20tabulating,%20and%20analyzing%20contacts%20between%20branched%20neuron%20morphologies.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20%20Identifying,%20tabulating,%20and%20analyzing%20contacts%20between%20branched%20neuron%20morphologies.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20%20Identifying,%20tabulating,%20and%20analyzing%20contacts%20between%20branched%20neuron%20morphologies.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20%20Identifying,%20tabulating,%20and%20analyzing%20contacts%20between%20branched%20neuron%20morphologies.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20%20Identifying,%20tabulating,%20and%20analyzing%20contacts%20between%20branched%20neuron%20morphologies.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20The%20Impact%20of%20Input%20Fluctuations%20on%20the%20Frequency-Current%20Relationships%20of%20Layer%205%20Pyramidal%20Neurons%20in%20the%20Rat.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20The%20Impact%20of%20Input%20Fluctuations%20on%20the%20Frequency-Current%20Relationships%20of%20Layer%205%20Pyramidal%20Neurons%20in%20the%20Rat.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20The%20Impact%20of%20Input%20Fluctuations%20on%20the%20Frequency-Current%20Relationships%20of%20Layer%205%20Pyramidal%20Neurons%20in%20the%20Rat.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20The%20Impact%20of%20Input%20Fluctuations%20on%20the%20Frequency-Current%20Relationships%20of%20Layer%205%20Pyramidal%20Neurons%20in%20the%20Rat.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20The%20Impact%20of%20Input%20Fluctuations%20on%20the%20Frequency-Current%20Relationships%20of%20Layer%205%20Pyramidal%20Neurons%20in%20the%20Rat.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20Morphological,%20electrophysiological.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20Morphological,%20electrophysiological.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20Morphological,%20electrophysiological.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20Morphological,%20electrophysiological.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20Disynaptic%20inhibition%20between%20neocortical%20pyramidal%20cells%20mediated%20by%20Martinotti%20cells.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20Disynaptic%20inhibition%20between%20neocortical%20pyramidal%20cells%20mediated%20by%20Martinotti%20cells.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20Disynaptic%20inhibition%20between%20neocortical%20pyramidal%20cells%20mediated%20by%20Martinotti%20cells.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2007%20-%20Disynaptic%20inhibition%20between%20neocortical%20pyra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%20-%20Fully%20Implicit%20Parallel%20Simulation%20of%20Single%20Neurons.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Fully%20Implicit%20Parallel%20Simulation%20of%20Single%20Neurons.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Inferring%20connection%20proximity%20in%20networks%20of%20electrically%20coupled%20cells.pdfhttp://bluebrain.epfl.ch/files/content/sites/bluebrain/files/Scientific%20Publications/2008%20-%20Inferring%20connection%20proximity%20in%20networks%20of%20electrically%20coupled%20cells.pdf 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8/2/2019 Anmol Seminar
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2. Maciokas, J. B., Goodman, P., Kenyon, J., Toledo-Rodriguez, M., & Markram, H. (2005).Accurate dynamical
models of interneuronal GABAergic channel physiologies. [doi: DOI: 10.1016/j.neucom.2004.10.083].
Neurocomputing, 65-66, 5-14.
3. Kalisman, N., Silberberg, G., & Markram, H. (2005).The neocortical microcircuit as a tabula rasa. Proc Natl
Acad Sci U S A, 102(3), 880-885.
4. Silberberg, G., Grillner, S., LeBeau, F. E. N., Maex, R., & Markram, H. (2005).Synaptic pathways in neural
microcircuits. [doi: DOI: 10.1016/j.tins.2005.08.004]. Trends in Neurosciences, 28(10), 541-551.
5. Richardson, M. J. E., Melamed, O., Silberberg, G., Gerstner, W., & Markram, H. (2005). Short-Term Synaptic
Plasticity Orchestrates the Response of Pyramidal Cells and Interneurons to Population Bursts.Journal of
Computational Neuroscience, 18(3), 323-331.
6. Toledo-Rodriguez, M., Goodman, P., Illic, M., Wu, C., & Markram, H. (2005). Neuropeptide and calcium-
binding protein gene expression profiles predict neuronal anatomical type in the juvenile rat. The Journal of
Physiology, 567(2), 401-413.
7. Grillner, S., Markram, H., De Schutter, E., Silberberg, G., & LeBeau, F. E. N. (2005).Microcircuits in actionfrom CPGs to neocortex. Trends in neurosciences, 28(10), 525-533.
2004
1. Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004).Interneurons of the
neocortical inhibitory system. [10.1038/nrn1519]. Nat Rev Neurosci, 5(10), 793-807.
2. Toledo-Rodriguez, M., Blumenfeld, B., Wu, C., Luo, J., Attali, B., Goodman, P., et al. (2004).Correlation maps
allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat
neocortex. Cereb Cortex, 14(12), 1310-1327.
3. Wang, Y., Toledo-Rodriguez, M., Gupta, A., Wu, C., Silberberg, G., Luo, J., et al. (2004).Anatomical,
physiological and molecular properties of Martinotti cells in the somatosensory cortex of the juvenile rat.J
Physiol, 561(Pt 1), 65-90.
2002
1. Maass, W., Legenstein, R., & Markram, H. (2002). A New Approach towards Vision Suggested by Biologically
Realistic Neural Microcircuit Models.In H. Blthoff, C. Wallraven, S.-W. Lee & T. Poggio (Eds.), Biologically
Motivated Computer Vision (Vol. 2525, pp. 1-6): Springer Berlin / Heidelberg.
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
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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.
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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