Presentation at the OpenAIRE-COAR Conference: "Open Access Movement to Reality: Putting the Pieces Together", Athens - May 21-22, 2014. Global collaborative neuroscience: Building the brain from big data, by Sean Hill - co-Director of Neuroinformatics in the Human Brain Project, École Poly­tech­nique Fédérale de Lau­sanne.

1. The Human Brain Project:
Building the brain from big data
!
Sean Hill
Co-Director, Blue Brain Project
Co-Director, Neuroinformatics, Human Brain Project
EPFL, Lausanne, Switzerland
!
!
Co-Executive Directors:
Henry Markram, Brain Mind Institute, EPFL, Switzerland
Karlheinz Meier, Kirchoff Institute, University of Heidelberg, Germany
Richard Frackowiak, Department of Clinical Neurosciences, CHUV,
Switzerland
!
www.humanbrainproject.eu

2. Upon this gifted age, in its dark hour,
Rains from the sky a meteoric shower
Of facts . . . they lie unquestioned, uncombined.
Wisdom enough to leech us of our ill
Is daily spun; but there exists no loom
To weave it into fabric;
Edna St. Vincent Millay, 1939

5. Developmental Disorders
• Autism spectrum disorders
• ADHD
• Learning disorders, conduct
disorders
• Strong genetic disorders
(Fragile X, Down’s etc)
Adolescent Disorders
• Depression, Suicide
• Eating disorders
• Bipolar disorder
• Conduct disorders and violence
• Borderline syndrome
• Adjustment disorders
• Anxiety, phobias, suicide
• Tourette’s syndrome
• Epilepsy
• Schizophrenia
• Epilepsy
• Mood disorders, hysterias,
anxieties and phobias
• Obsessive compulsive disorders
• Eating disorders, sexual disorders
• Sleep disorders, stress disorders
• Impulse control disorders
• Substance abuse disorders
• PTSD/TBI
Adult Disorders
• Depression
• Dementia
• Neurodegenerative disorders
• Alzheimer’s
• Parkinson’s
• Huntington’s
• Memory disorders
Aging Disorders
Glutamate
!
Nutrition
!
Dopamine
!
Genes
!
Sugar
!
GABA
!
Myelin
!
Serotonin
!
Metals
!
Dopamine
!
Toxins
!
Acetylcholine
!
Protein misfolding

6. Microarrays Electron
Microscopy
Confocal
Microscopy
Single
Cell
PCR
Protein
quan8fica8on
Magne8c
bead
Gene
sequencing
Gene
silencing
Gene
over-­‐
expression Gene8c
vectors
Two-­‐hybrid
system
Protein
separa8on
Wholecell
&
Inside-­‐Out
Patch
Laser
micro-­‐
dissec8on
Cell
culture Fluorescence
microscopy
Cellular
tracing
Cell
sor8ng
In
situ
hybridiza8on
Rhodopsin
vectors
Immuno-­‐detec8on
amplified
by
T7 Mass-­‐spectroscopy
Organelle
transfec8on
Spa8al
Proteomics
Immuno-­‐
staining
Mul8
Electrode
Array
Extracellular
Recording
Dye
Imaging 2DE
proteomics Tissue
transfec8on
Enzyma8c-­‐ac8vity
measurement
Behavioral
Studies Ultramicroscopy
Magnet
Resonance
Diffusion
Imaging fMRI EEG Transgenic
lines

7. What is the Human Brain Project?
7
A 10-year European initiative to
launch a global, collaborative effort
to understand the human brain,
enabling advances in neuroscience,
medicine and future computing.
!
One of the two final projects
selected for funding as a FET
Flagship from 2013.
!
Officially launched October 1, 2013.
!
A consortium of 256 researchers
from 135 research groups, 81 partner
institutions in 22 countries across
Europe, America and Asia.
!
€ 74M funding for the first 30
months. €1 billion/10 years.

8. What is a FET Flagship?
8
Future and Emerging Technologies
(FET) Flagships are ambitious large-
scale, science-driven, research
initiatives
that aim to achieve a visionary goal.
!
The scientific advance should provide a
strong and broad basis for future
technological innovation and
economic exploitation in a variety of
areas, as well as novel benefits for
society.
!
Objective is to keep Europe
competitive and drive technological
innovation

9. Goal
9
The Human Brain Project should:
!
•Lay the technical foundations for a
new model of ICT-based brain
research
!
•Drive integration between data and
knowledge from different disciplines
!
•Catalyze a community effort to
achieve a new understanding of the
brain, new treatments for brain
disease and new brain-like computing
technologies.

10. Research Areas
10
Neuroscience
!
Integrate everything we know about the
brain into computer models and
simulations
!
!
Medicine
!
Contribute to understanding, diagnosing
and treating diseases of the brain
!
!
Future Computing
!
Learn from the brain to build the
supercomputers of tomorrow

11. Scientific organization
11
Organisation of
HBP Scientific
& Technological
Work
Divisions
Molecularand
CellularNeuroscience
CognitiveNeuroscience
Theoretical
Neuroscience
Neuroinformatics
BrainSimulation
HighPerformance
Computing
MedicalInformatics
Neuromorphic
Computing
Neurorobotics
Mouse Data
Human Data
Cognition Data
Theory
NIP
BSP
HPCP
MIP
NMCP
NRP
Applications
for neuroscience
Applications
for medicine
Applications for
future computing
Scientificandtechnologicalresearch
Data
Platform
building
Platform
use

12. Data
12
Generate and interpret
strategically selected data
needed to build multilevel
atlases and unifying
models of the brain.
nism, and we can even begin to look for
potential therapeutic targets along it.
This process is intensely iterative. We
integrate all the data we can find and pro-
gram the model to obey certain biological
rules, then run a simulation and compare
the “output,” or resulting behavior of pro-
teins, cells and circuits, with relevant ex-
perimental data. If they do not match, we
go back and check the accuracy of the data
and refine the biological rules. If they do
match, we bring in more data, adding ever
more detail while expanding our model to
a larger portion of the brain. As the soft-
ware improves, data integration becomes
faster and automatic, and the model be-
haves more like the actual biology. Model-
ing the whole brain, when our knowledge
of cells and synapses is still incomplete, no
longer seems an impossible dream.
To feed this enterprise, we need data
and lots of them. Ethical concerns restrict
the experiments that neuroscientists can
perform on the human brain, but fortu-
nately the brains of all mammals are built
according to common rules, with species-
specific variations. Most of what we know
about the genetics of the mammalian brain
comes from mice, while monkeys have giv-
en us valuable insights into cognition. We
can therefore begin by building a unifying
model of a rodent brain and then using it
as a starting template from which to de-
velop our human brain model—gradually
integrating detail after detail. Thus, the
models of mouse, rat and human brains
will develop in parallel.
The data that neuroscientists generate
will help us identify the rules that govern
brain organization and verify experimen-
tally that our extrapolations—those pre-
dicted chains of causation—match the bi-
ological truth. At the level of cognition, we
know that very young babies have some
grasp of the numerical concepts 1, 2 and 3
but not of higher numbers. When we are
finally able to model the brain of a new-
born, that model must recapitulate both
what the baby can do and what it cannot.
A great deal of the data we need al-
ready exist, but they are not easily accessi-
ble. One major challenge for the HBP will
be to pool and organize them. Take the
medical arena: those data are going to be
immensely valuable to us not only be-
cause dysfunction tells us about normal
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Circuits
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Regions
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13. A Data Ladder to the Human Brain
13

14. However,
!
HBP is NOT primarily a data generation project
!
It IS a data integration project.

15. What I cannot create, I do not understand.!
-Richard Feynman

16. Build, Simulate and Validate Unifying Brain Models
16
208.17
0 100 0 100 0 100 0 100 0 100 200
L6L5L4L2/3L1/2
L6
L5
L4
L2
L1
L3
Stimulation Site
Firing Cell Count
Experimental Data Gathering
Supercomputing
10 ms
50 mV
Vm
Istim
4 nA
Simulation
Model Building
Model Validation
Refinement of
Models and Experiments
Worldwide Published Data,
Models and Literature
Analysis and
Visualization
Mouse
atlas
data
courtesy
of
Allen
Ins8tute
for
Brain
Science

17. Theory
17
FIGURE 13
The Integrative Role of Theory in the HBP
Theories of computing
Theories of information processing
Theories of cognition
Principles of Neural Computation
Bridging layers of biology
Statistical validation
Mathematical formulations
Numerical methods
Statistical predictive models
Data Analysis
Conceptual Predictions
Molecular, Biophysical & Phenomenological Models
Neuroinformatics
Neuroscience
Brain Tissue
Learning
& memory
Cognition
& Behavior
Neurons Synapses Circuits
Multiscale
models
Simplified
models
Neuromorphic
implemetation
Robotics
Implementation

18. ICT Platforms
18
An integrated network of ICT platforms of the HBP
Figure 48: The HBP Platforms – physical infrastructure. Neuromorphic Computing: Heidelberg, Germany & Manchester, United Kingdom;
Neurorobotics: Munich, Germany; High Performance Computing: Julich, Germany & Lugano, Switzerland, Barcelona, Spain & Bologna, Italy;
Brain Simulation: Lausanne, Switzerland; Theory Institute: Paris, France
Six new open access ICT
platforms:
!
1. Neuroinformatics
2. Brain Simulation
3. Medical Informatics
4. High Performance
Computing
5. Neuromorphic Computing
6. Neurorobotics
!
For the entire research
community.

19. Neuroinformatics Platform
19
Provide technical capabilities to federate
neuroscience data, analyze structural and
functional brain data and to build and
navigate multi-level brain atlases. This
involves:
!
•spatial and temporal data registration
•ontology development and semantic
annotation
•predictive neuroscience
•machine learning, data mining
•track provenance, build workflows.
!
Goal: enable an integrated view of the
neuroscience data. Prepare data for
modeling pipelines

20. Multiscale and Multimodal Brain Atlases
20
Data registered to standard coordinate spaces:
- collections of spatially and semantically registered
and searchable data, models and literature
Mouse
atlas
data
courtesy
of
Allen
Ins8tute
for
Brain
Science

21. • Share data: Secure access, authorization, collaborative
• Curate data: Data annotation, quality control, consistency check
• Publish data: Data citation, community rating and reviews
• Preserve data: Versioning, archive, mirror, replicate
• Federate data: Tens to hundreds of thousands of members of the
federation
• Federated search: Search over all data, free text, ontology-based or spatial
search
• Ease of use: Lightweight! Dropbox-like functionality, easy metadata
annotation, portal for search
• Access data: Object-based view of data, semantic annotations, data
models, flexible metadata
• Analyze and integrate data: Support data analysis, workflow building,
provenance tracking
21
Requirements:
Global Neuroinformatics Infrastructure

22. 22
Global shared data (INCF DataSpace)
Data access, query
and provenance services
Portals, Search, Applications
Analytic
Services
Neuroinformatics Infrastructure
Digital
Atlasing
services
Private LabSpace
(Curated data)
Global Public
KnowledgeSpace
(Published data)

23. Data management strategy
23
DATASPACE
• Don’t centralize - federate
• INCF Global datasharing
infrastructure
• Federated data
management
• Dropbox-like Ease of Use
• Highly scalable - index
petabytes of data
• Robust Infrastructure
dataspace.incf.org

24. 24
MIRROR
METADATA
ACROSS
FOUR
AMAZON
ZONES
US-­‐WEST US-­‐EAST EU-­‐WEST AP-­‐EAST
Cloud Server
8 GB memory; 4 cores
850 GB instance storage
64-bit platform
I/O Performance: 500 Mbps
Dataspace
metadata
Cloud
Server A
Dataspace
metadata
Cloud
Server B
Dataspace
metadata
Cloud
Server C
Dataspace
metadata
Cloud
Server D

25. Knowledge integration strategy
25
neurolex.org
•INCF Community encyclopedia
•Living review articles
•Curated data
•Build and maintain working
ontologies
•Links to data, models and literature
•Define all vocabulary, terms,
protocols, brain structures, diseases,
etc
•Semantic organization, search,
analysis and integration
•Global directory of all shared
vocabularies, CDEs, etc

26. Semantic search of data
Search for data through semantic relationships
•Producer
•Laboratory
•Experimenter
•Protocol
•Solutions
•Equipment
•Measurements
•Specimen
•Species
•Strain
•Age
•Location
•Brain region name
•X,Y,Z location
•Data sample
•Format
•Community rating
•Extracted features
•Analysis

28. Semantically annotated digital data publications

29. Semantically annotated digital data publications
Brain volume descriptions

30. Semantically annotated digital data publications
Methods

31. Semantically annotated digital data publications
Data with extracted features

32. Semantically annotated digital data publications
Overview statistics and classifications

33. Semantically annotated digital data publications
Cell densities

34. Semantically annotated digital data publications
Gene expression

35. Semantically annotated digital data publications
Neuron morphologies

36. Semantically annotated digital data publications
Electrophysiology

37. Semantically annotated digital data publications
Synaptic connectivity

38. Automated Analysis
38

39. Morphology Analysis and
Classification
39

40. Brain Simulation Platform
40
Provide technical capabilities to build and
simulate multi-scale brain models at
different levels of detail.
•internet portal for neuroscientists
•modeling tools
•workflows
•simulation
•virtual instruments (EM, LFP, fMRI, etc)
•link to virtual body and environment
•in silico experiments
Goal: Integrate large volumes of
heterogeneous data in multi-scale models of
the mouse and human brains, and to
simulate their dynamics.
Enable neuroscientists to ask new
questions and prioritize experiments.

41. Integration of laboratory data
41
S1L1 Neocortical
Microcircuit
30’000 neurons
400
Electrical
Behaviors
Gene
profiles
Synap.c
profiles
Morphology
profiles
Connec.vity
profiles
Electrical
profiles
Circuit
profiles
Protein
profiles

42. Brain Simulation Platform
42
Data-driven biophysical single cell models
Hay et al., PLOS Comp. Biology, 2012

43. Building a cortical microcircuit
43

44. 44
Predictive Neuroscience: One example
KS*
Som
a
A
t
KS*
Som
a
A
KS*
KS*
Som
a
A
t
KS* KS*
Som
a
A
Som
a
A
t
Som
a
A
t
KS*
Som
a
A
t
Som
a
A
t
Hill
et
al.
PNAS
2012
2,970 possible synaptic pathways
in a cortical microcircuit alone.
!
22 have been characterized.
!
Can we identify principles
to predict the rest?

45. Brain Simulation Platform
45
Cortical microcircuitry

46. Brain Simulation Platform
46
Cortical microcircuitry and local field potential
Reimann et al., Neuron, 2013 in collaboration with the Allen Institute, Seattle, WA

47. 230μm thick
A virtual brain slice

48. Spontaneous activity in a virtual brain slice
230μm thick

49. Virtual slice: evoked activity, 20Hz stimulation

50. Virtual slice: evoked activity, 40Hz stimulation

51. 51
U
N
C
O
R
R
E
C
T
E
D
P
R
O
O
F
311 variance between different lines (C57BL/6 and Kunming) of mouse
312 seems greater than the variance among different individuals in the
313 same line (C57BL/6).
314 To compare the laminar distribution in different cortical regions, we
315 extracted the image stacks of the other two cortical regions in the left
316 hemisphere of a C57 mouse brain, the posteromedial barrel subfield
317 (PMBSF, AP: Bregma, −0.22 mm, ML: Bregma, +3.25 mm, DV:
318 Bregma, +2 mm) and the primary visual cortex (V1, AP: Bregma,
319 −3.88 mm, ML: Bregma, +2.5 mm, DV: Bregma, +1.25 mm). The
320 inner cells and blood vessels were vectorized and reconstructed (Sup-
321 plementary Figs. 5a–e). In the barrel cortex, the vascular diameters
322 were estimated both in vivo and in vitro for evaluation (Supplementary
323 Fig. 6e). The estimated diameter histogram showed that the diameter
324 distribution peaked in 3–6 μm both in vivo and in vitro, which is also
325 in good agreement with the histograms from Tsai et al. (2009). Note
326 that a penetrating vessel had a sinuous path in the middle layer of the
327 barrel cortex (Supplementary Fig. 5b), which is consistent with an ob-
328 servation in the human brain (Duvernoy et al., 1981). The cellular and
329 vascular distribution parameters were computed along the cortical
330 axial direction (Fig. 5). As expected, the cell number density varied
331 greatly among different cortical regions, especially near layer IV
332 (Fig. 5a). The densities in the granular (PMBSF), and partially granular
333cortex (V1), were relatively higher than in the agranular cortex (M1).
334The smallest distances from cells to the nearest blood vessels were
335near the middle layers (Fig. 5b). For the vascular length densities
336(Fig. 5c) and microvascular fractional volumes (Fig. 5d), the peak values
337were also located near layer IV. Interestingly, we noticed that the vascu-
338lar density values in layers I, V and VI were similar (Figs. 5c, d). For
339quantitative evaluation, we computed the standard deviation of three
340cortical regions in different layers and found that the variation in vascu-
341lar density was significantly higher in the middle layers (20%–50% cor-
342tical depth, near layer II/III and IV) than in the upper and lower layers
343(p b 0.01).
344Areal configurations of cells and blood vessels
345Mainly limited by imaging depth, most of the 3D optical imaging
346and quantitative analysis techniques was restricted in the part of the
347cerebral cortex (about 1 mm3
), leaving the deeper brain areas largely
348uncharted. Our customized protocol enabled us to analyze the detailed
349cellular and vascular configurations on a brain-wide scale. We
350extracted three additional image stacks in the mouse brain: frontal
351association cortex (FrA, AP: Bregma, +2.58 mm, ML: Bregma, +1.5 mm,
352DV: Bregma, +1.5 mm), striatum (AP: Bregma, +0.02 mm, ML: Bregma,
Fig. 4. Reconstruction and colocalization of vectorized cells and blood vessels in M1. (a) 3D reconstruction of the cytoarchitecture in M1. The green points represent detected cellular cen-
troids. (b) The surface rendering of the vasculature shows how some large vessels penetrate into the cerebral cortex. The curved tubes represent the traced blood vessels, and the diameters
of the blood vessels are encoded as indicated by the color bar. The white arrow indicates a large penetrating vessel, the diameter of which exceeds 60 μm. The pial vascular network was
manual labeled and rendered to give an anatomical context. The size of the gray bounding box is 600 μm × 600 μm × 920 μm. (c) An enlarged view of the white box in (a, b) shows
colocalization of the cells (green) and blood vessels (red). The size of the white bounding box is 200 μm × 200 μm × 100 μm.
6 J. Wu et al. / NeuroImage xxx (2013) xxx–xxx
Please cite this article as: Wu, J., et al., 3D BrainCV: Simultaneous visualization and analysis of cells and capillaries in a whole mouse brain with
one-micron voxel resoluti..., NeuroImage (2013), http://dx.doi.org/10.1016/j.neuroimage.2013.10.036
Wu
et
al,
Neuroimage,
2013
E
C
T
E
D
P
R
O
O
F
ously visualize the cells and blood vessels. (a) 3D reconstruction of the whole mouse brain with a corner-cut view. (b, c) Sagittal and
ructures of the whole mouse brain. Note that the background intensity is uniform. (d) Minimum intensity projection of (c) (thick-
e. (e) Maximum intensity projection of (c) (thickness: 200 μm, posterior direction) shows vascular architecture. (f) Enlarged view of
simultaneous cross-sections of cells (black) and blood vessels (white). (g) The enlarged view of the cortical region indicated by the
5J. Wu et al. / NeuroImage xxx (2013) xxx–xxx
R
O
O
F
J. Wu et al. / NeuroImage xxx (2013) xxx–xxx
In progress: Modeling Neural Glial Vasculature Coupling
Bruno
Weber,
University
of
Zürich

52. 52
In progress: Scaling infrastructure to the whole rodent brain

53. High Performance Computing Platform
53
Provide the project and the community
with:
•The computing power necessary to
build and simulate models of the brain.
•Develop new supercomputing
technology, up to the exascale
•Drive new capabilities for interactive
computing and visualization.

54. High Performance Computing Challenges
54
Computa.onal
Complexity
Memory
Requirements
O(1
MB)
O(10
GB)
O(1
TB)
O(100
TB)
O(100
PB)
Single
Cell
Model
Cellular
Neocor.cal
Column
O(10,000x
cell)
Cellular
Mesocircuit
O(100x
NCC)
Cellular
Rat
Brain
O(100x
Mesocircuit)
Cellular
Human
Brain
O(1,000x
Rat
Brain)
O(Gigaflops) O(10
Teraflops) O(100
Teraflops) O(1
Petaflops) O(1
Exaflops)
EPFL
4-­‐rack
BlueGene/L
CADMOS
4-­‐rack
BlueGene/P
Reac.on-­‐Diffusion
O(1,000x
memory)
Reac.on-­‐Diffusion
O(1,000x
memory)
Reac.on-­‐Diffusion
O(1,000x
memory)
Glia-­‐Cell
Integra.on
O(10x
memory)
Vasculature
O(1x
memory)
Par.cles
O(10-­‐100x
memory)
Reac.on-­‐Diffusion
O(1,000x
memory)
Glia-­‐Cell
Integra.on
O(10x
memory)
Vasculature
O(1x
memory)
Reac.on-­‐Diffusion
O(100-­‐1,000x
performance)
Plas.city
O(1-­‐10x
performance)
Local
Field
Poten.als
(1x
performance)
Reac.on-­‐Diffusion
O(100-­‐1,000x
performance)
Glia-­‐Cell
Integra.on
O(1-­‐10x
performance)
Vasculature
O(1x
performance)
Electric
Field
Interac.on
O(1-­‐10x
performance)
Plas.city
O(1-­‐10x
performance)
Reac.on-­‐Diffusion
O(100-­‐1,000x
performance)
Glia-­‐Cell
Integra.on
O(1-­‐10x
performance)
EEG
(1-­‐10x
performance)
Vasculature
O(1x
performance)
Plas.city
O(1-­‐10x
performance)
Behavior
O(10-­‐100x
performance)
Reac.on-­‐Diffusion
O(100-­‐1,000x
performance)

55. Medical Informatics
55
•Federate clinical data from
hospital archives and proprietary
databases, while providing strong
protection for patient data.
!
•Enable researchers to develop
data-driven “biological
signatures”of diseases.
!
•Develop new approaches to
understanding the causes of
disease and identifying effective
treatments

56. Disease signatures
56

57. Disease signatures
57

58. Neuromorphic Computing Platform
58
Simulate models on low power
hardware chips. Build off of
BrainScaleS and Spinnaker projects
to provide ability to run large-scale
simulations at or beyond real time
with low power consumption.

59. Neurorobotics Platform
59
Enable closed-loop simulations
of behavior. Virtual bodies,
sensory input and environments
to couple with the simulations.
This platform is key to providing
sensory input to the simulations
and depicting the motor
outputs.

60. Applications: Understanding principles of cognition
60
Figure 28: Use of the HBP platforms to study mechanisms and principles of cognition: comparing simulated and biological humans
and mice on the same cognitive task (touchscreen approach)
Use Case 1: Tracing causal mechanisms of cognition
Benchmark
species
comparison
In silico
species
comparison
Simulation-
robot
closed loop
Simulation-
robot
closed loop
Virtual-
biological
comparison
Virtual-
biological
comparison

61. Applications: Developing new drugs for brain disorders
61
Use Case 2: Developing new drugs for brain disorders
Model
of healthy brain
Model
of diseased brain
Target
identification
New drug
Clinical trials
Side effects
In silico
search for treatments
Computational chemistry &
Molecular Dynamics simulations
Preclinical trials
Binding kinetics
Multi-level biological
signature of disease

62. Applications: Developing neuromorphic controllers
62
Use Case 3: Developing neuromorphic controllers
for car engines
Figure 30: Use of the HBP platforms to accelerate the development
Biologically detailed brain model
SELECT
the most suitable
models of cognitive
circuitry
EXPORT
simplified brain
model and learning
rules to generic
neuromorphic system
Simplified brain model
TRAIN
neuromorphic circuit rapidly in closed
loop with simulated robot in simulated
environment
EXTRACT
custom, compact,
low-power,
low-cost
neuromorphic
designs

63. Cross-cutting Activities
•Explore ethical, social and philosophical implications
•Engage public in dialogue
63
•Provide young scientists with transdisciplinary knowledge
and skills
•Generate widespread awareness of results, open access
policy (publications, data, software tools)
•Ensure a cohesive and integrative effort
•Collaborate with other European and international efforts
Ethics
Education
Dissemination
Governance

64. Participating Scientists
64

65. The HBP Competitive Calls Programme
65
~20% of funding (~200M€/10 years) allocated to open calls

66. The Initial HBP Consortium
Figure 40: Research institutions identified to participate in the HBP, if approved as a FET Flagship Project
Initial HBP Consortium
Europe
United States
and Canada
Argentina
Israel
Japan
China
66
For more information and
a full list of leaders, partners
and collaborators
!
please visit:
www.humanbrainproject.eu