OpenAIRE-COAR conference 2014: Global collaborative neuroscience - Building the brain from big data, by Sean Hill - Human Brain Project
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OpenAIRE-COAR conference 2014: Global collaborative neuroscience - Building the brain from big data, by Sean Hill - Human Brain Project

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Presentation at the OpenAIRE-COAR Conference: "Open Access Movement to Reality: Putting the Pieces Together", Athens - May 21-22, 2014. ...

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.

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OpenAIRE-COAR conference 2014: Global collaborative neuroscience - Building the brain from big data, by Sean Hill - Human Brain Project OpenAIRE-COAR conference 2014: Global collaborative neuroscience - Building the brain from big data, by Sean Hill - Human Brain Project Presentation Transcript

  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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.
  • 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
  • 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
  • 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 !"#$%&'()$*+(,)$-+./#01 Molecular !"#$%&'()"*+"($,$-(#./"0$12%%2%1"" '%3$("-"42#(*,#*5$/"6*'73"&(-%,7-&$" 2%&*"-"3212&-7"+-#,2427$"&.-&"#*402%$," #*45*%$%&"4*7$#'7-("5-(&,"&*" -,,$4"07$"-"#$77"&.-&"3$4*%,&(-&$," &.$"$,,$%&2-7"5(*5$(&2$,"*+"-"%$'(*%/" &.$"&(-%,42,,2*%"*+"$7$#&(2#-7"-%3" #.$42#-7",21%-7,8" Cellular !"0(-2%92%9-90*:",24'7-&2*%"6277" .-;$"&*"#-5&'($"$;$()"3$&-27"*+"" %$'(*%,"-%3"%*%%$'(*%-7"172-7"" #$77,/"2%#7'32%1"&.$"$:-#&"1$*4$&(2#" ,.-5$,"*+"&.$2("3$%3(2&$,"-%3"-:*%," &.-&"($#$2;$"-%3",$%3"2%+*(4-&2*%8"" Circuits !"4*3$7"*+"&.$"%$'(-7"#*%%$#&2*%," -4*%1"%$21.0*(2%1"#$77,"4-)" +'(%2,."#7'$,"&*"&.$"*(212%,"*+" #*457$:"0(-2%"32,$-,$,",'#."-," -'&2,4"-%3",#.2<*5.($%2-8" Whole Organ !%"2%",272#*"0(-2%"421.&",'0,&2&'&$" +*("&.$"-#&'-7"*(1-%8"=)"($4*;2%1" &.$"#*45'&$("#*3$"+*("-">1$%$/?"" &.$";2(&'-7",),&$4"#-%/"+*("2%,&-%#$/" " -,",#2$%&2,&,"3*"&*3-)"0)">@%*#@2%1" *'&?"-"1$%$"2%"42#$8"A.$"&**7"6*'73" -;*23"&.$"7$%1&.)"0($$32%1"5(*#$,," -%3"#*'73",24'7-&$"-"4'7&2&'3$"" *+"$:5$(24$%&-7"#*%32&2*%,8"" Regions B-C*("%$'(-7",'0,&('#&'($,D" &.$"-4)13-7-"E$4*&2*%,F/"&.$" .255*#-45',"E4$4*()F/"&.$"" +(*%&-7"7*0$,"E$:$#'&2;$"#*%&(*7FD #-%"0$"2%,5$#&$3"-7*%$"*("-,"&.$)" 2%&$(-#&"62&."*%$"-%*&.$(8""
  • A Data Ladder to the Human Brain 13
  • However, ! HBP is NOT primarily a data generation project ! It IS a data integration project.
  • What I cannot create, I do not understand.! -Richard Feynman
  • 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
  • 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
  • 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.
  • 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
  • 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
  • • 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 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)
  • 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 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
  • 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
  • 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
  • Semantically annotated digital data publications
  • Semantically annotated digital data publications Brain volume descriptions
  • Semantically annotated digital data publications Methods
  • Semantically annotated digital data publications Data with extracted features
  • Semantically annotated digital data publications Overview statistics and classifications
  • Semantically annotated digital data publications Cell densities
  • Semantically annotated digital data publications Gene expression
  • Semantically annotated digital data publications Neuron morphologies
  • Semantically annotated digital data publications Electrophysiology
  • Semantically annotated digital data publications Synaptic connectivity
  • Automated Analysis 38
  • Morphology Analysis and Classification 39
  • 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.
  • 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
  • Brain Simulation Platform 42 Data-driven biophysical single cell models Hay et al., PLOS Comp. Biology, 2012
  • Building a cortical microcircuit 43
  • 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?
  • Brain Simulation Platform 45 Cortical microcircuitry
  • Brain Simulation Platform 46 Cortical microcircuitry and local field potential Reimann et al., Neuron, 2013 in collaboration with the Allen Institute, Seattle, WA
  • 230μm thick A virtual brain slice
  • Spontaneous activity in a virtual brain slice 230μm thick
  • Virtual slice: evoked activity, 20Hz stimulation
  • Virtual slice: evoked activity, 40Hz stimulation
  • 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 In progress: Scaling infrastructure to the whole rodent brain
  • 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.
  • 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)
  • 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
  • Disease signatures 56
  • Disease signatures 57
  • 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.
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Participating Scientists 64
  • The HBP Competitive Calls Programme 65 ~20% of funding (~200M€/10 years) allocated to open calls
  • 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