Vision Document HBP.pdf

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FP7 604102 HBP CP-CSA-FF

604102 (HBP) Annex 1 Part B, version of August 16, 2013 Appendices Page 1 of 78

Appendix 1: Overall Vision for the Human Brain Project


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TABLE OF CONTENTS

TABLE OF CONTENTS ...................................................................................................................................................... 2!1! Strategic S&T roadmap ................................................................................................................................................. 3!

1.1! Conceptual overview ................................................................................................................................................ 3!1.2! The HBP work plan .................................................................................................................................................. 4!

1.2.1! Data ..................................................................................................................................................................... 6!1.2.2! ICT platforms .................................................................................................................................................. 17!1.2.3! Applications ..................................................................................................................................................... 35!1.2.4! SP14: The HBP Ethics and Society Programme ......................................................................................... 41!1.2.5! SP15: Project and programme management, education, dissemination and innovation .................... 43!

1.3! Risks and contingencies ......................................................................................................................................... 46!2! HBP as a European Programme: collaboration and coordination ........................................................................ 52!

2.1! Forms of collaboration ........................................................................................................................................... 52!2.2! Governing the collaboration ................................................................................................................................. 52!

3! Impact ............................................................................................................................................................................ 52!3.1.1! Transformational impact ............................................................................................................................... 52!3.1.2! Future neuroscience ....................................................................................................................................... 53!3.1.3! Future medicine .............................................................................................................................................. 55!3.1.4! Future computing ........................................................................................................................................... 56!3.1.5! Benefits for European industry and the European economy ................................................................... 59 !3.1.6! Benefits for European society ........................................................................................................................ 60!3.1.7! Strengthening of the interfaces between ICT and other disciplines ........................................................ 61 !3.1.8! Specific impacts of the ICT Platforms ......................................................................................................... 61!3.1.9! Exploitation and IPR ...................................................................................................................................... 66!3.1.10! Use of the platforms ..................................................................................................................................... 67!3.1.11! Sustainability ................................................................................................................................................. 67!3.1.12! Additional Financial resources ................................................................................................................... 68!

4! Ethical, societal and gender policies of the Flagship ............................................................................................... 68!4.1! Ethics and society ................................................................................................................................................... 68!4.2! Governance of ethical issues within the HBP Project ....................................................................................... 69 !

4.2.1! Strategic oversight ........................................................................................................................................... 69!4.2.2! Research ethics ................................................................................................................................................ 69!

4.3! Gender aspects ........................................................................................................................................................ 69!4.3.1! Gender balance in HBP recruitment and management ............................................................................ 69!4.3.2! Gender considerations in HBP research ..................................................................................................... 70!

REFERENCES ...................................................................................................................................................................... 71!


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1 Strategic S&T roadmap

1.1 Conceptual overview

The Human Brain Project is a ten-year project, consisting of a thirty-month ramp-up phase, funded under

FP7, with support from a special flagship ERANET, and a ninety-month operational phase, to be funded

under Horizon 2020. The project, which will have a total budget of over Euro 1 billion, is European-ledwith a strong element of international cooperation. The goal of the project is to build a completely new

ICT infrastructure for neuroscience, and for brain-related research in medicine and computing, catalysing

a global collaborative effort to understand the human brain and its diseases and ultimately to emulate its

computational capabilities.

The proposed infrastructure will consist of six ICT-based research platforms, providing neuroscientists,

medical researchers and technology developers with access to highly innovative tools and services that can

radically accelerate the pace of their research. These will include a Neuroinformatics Platform, that links to

other international initiatives, bringing together data and knowledge from neuroscientists around the world

and making it available to the scientific community; a Brain Simulation Platform, that integrates this

information in unifying computer models, allowing in silico experiments, impossible in the lab; a High

Performance Computing Platform that provides the interactive supercomputing technology neuroscientists

need for data-intensive modelling and simulations; a Medical Informatics Platform that federates clinical

data from around the world, providing researchers with new mathematical tools to search for biological

signatures of disease; a Neuromorphic Computing Platform that makes it possible to translate brain models

into a new class of hardware devices and to test their applications; a Neurorobotics Platform, allowing

neuroscience and industry researchers to experiment with virtual robots controlled by brain models

developed in the project. The platforms are all based on previous pioneering work by the partners and will

be available for internal testing within eighteen months of the start of the project. Within thirty months, the

platforms will be open for use by the community, receiving continuous upgrades to their capabilities, for the

duration of the project.

The HBP will trigger and drive a global, collaborative effort that uses the platforms to addressfundamental issues in future neuroscience, future medicine and future computing. A significant andsteadily growing proportion of the budget will be devoted to research by groups outside the originalHBP Consortium, working on themes of their own choosing. The expected end results will include anew understanding of the brain and its diseases and radically new forms of ICT, that exploit thisknowledge. The social economic and industrial impact is potentially enormous.

To achieve these goals the project has set itself six Strategic Objectives for the Full Flagship (SOFF).

SOFF-1. Design, develop, deploy and operate the ICT platforms, providing novel ICT-based servicesfor researchers in neuroscience, medicine and computing and creating a user community ofresearch groups from within and outside the HBP.

SOFF-2. Catalyse ground-breaking research into the structure and function of the human brain, thecauses, diagnosis and treatment of brain disease, and brain-inspired computing technology

SOFF-3. Generate and collect the strategic neuroscience data, create the theoretical frameworks anddevelop the scientific and technological capabilities required for the design and operation of

the platforms

SOFF-4. Implement a strategy of responsible innovation, monitoring science and technological resultsas they emerge, analysing their social and philosophical implications, raising awareness of

these issues among researchers and among citizens and involving them in a far-reaching

conversation about future directions of research.

SOFF-5.

Implement a programme of transdisciplinary education, training young European scientiststo exploit the convergence between ICT and neuroscience and creating new capabilities for

European industry and academia.


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SOFF-6. Develop a framework for collaboration that links the partners under strong scientificleadership and professional project management, providing a coherent European approach

and promoting effective alignment of regional, national and European research and

programmes.

1.2 The HBP work plan

The HBP is organized into fifteen subprojects (thirteen in the ramp-up phase) covering five areas of research.Each subproject builds on existing work by the partners building, contributes to work in the other subprojects

and acts as a catalyst for the integration of research from outside the HBP Consortium. Below we briefly

summarise our specific aims in each area.

1. Data. This part of the HBPs work aims to generate and analyse strategically selected data on thestructure and function of the mouse and human brains at different levels of biological organisation

(genetics, gene expression, cell numbers and morphologies, long-range connectivity, cognitive

function etc.), deriving general principles of brain organisation. An important part of the work will

focus on data (e.g. genetic data, data on gene expression in different cell types) that the project will

subsequently use to predict features of the brain that are difficult or impossible to measure

experimentally. The results, deposited in publicly accessible atlases (see below), will feed multi-level

models of the mouse and human brain. Another, equally important part of the work will focus oncognitive function combining human data from fMRI, DTI, EEG and other non-invasive techniques

to provide a comprehensive spatial and temporal description of the neuronal circuits implicated in

specific well- characterised cognitive tasks. The results will guide the development of high-level

models of neuronal circuitry. High-level models will be used to validate biologically detailed models

and vice versa, combining the advantages of top-down and bottom-up approaches.

2. Theory. HBP work in theoretical neuroscience will investigate the mathematical principles under-lying the relationships between different levels of brain organisation and the plasticity mechanisms

that subserve the acquisition, representation and long-term memorisation of information about the

outside world. The results will help to identify the critical data needed for modelling, and to simplify

detailed brain models for implementation in IT and specifically in neuromorphic computing systems.

The planned work includes the creation of a new European Institute for Theoretical Neurosciencewith programmes for visiting scientists and young investigators.

3. ICT Platforms. The HBP will build, operate and continuously update an integrated system of six ICTplatforms providing high-quality services to researchers and technology developers inside and outside

the HBP. All the platforms will be remotely accessible through a single HBP web portal.

a. Neuroinformatics Platform. The Neuroinformatics Platform will use state-of-the-art ICT(semantic technology, distributed query technology, provenance tracking etc.) to give

neuroscientists the ability to organise and search massive volumes of heterogeneous data,

knowledge and tools produced by the international neuroscience community. New tools

incorporated in the platform will allow researchers to analyse and interpret large volumes

of structural and functional data and to construct brain atlases. The HBP will use these

tools to develop detailed 3D multi-level atlases of the mouse and human brains. The

atlases, accessible to the community through the HBP web portal, will be the main sourceof high-quality annotated data for brain modelling.

b. Brain Simulation Platform. The Brain Simulation Platform will provide a suite ofsoftware tools and workflows that allow researchers to build and simulate models of the

brain at different levels of description, and to perform in silico experiments that are

difficult or impossible in the lab. The project will use the platform to develop and validate

first draft models of different levels of brain organisation, in mice and in humans. The

ultimate goal will be to build and simulate multi-scale, multi-level models of the whole

mouse brain and the whole human brain. The capabilities made available by the platform

will contribute to identifying the neuronal architectures underlying specific brain

functions, to studies of the mechanisms underlying neurological and psychiatric disease,

and to new simulation-based techniques of drug discovery. Simplified versions of brainmodels will form the basis for novel neuromorphic computing systems.


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c. High Performance Computing Platform . The High Performance Computing Platformwill provide the advanced supercomputing capabilities required for brain modelling and

simulation and for the design of novel neuromorphic computing systems. The first

element in the platform will be the HBP Supercomputer , a machine that will gradually

evolve toward the exascale over the duration of the project. This will be complemented

by satellite facilities dedicated to software development, molecular dynamics simulations,

and massive data analytics. A key goal will be to develop a capability for in situ analysis

and visualisation of exascale data sets and for interactive visual steering of simulations.These features will be invaluable not just for brain simulation but also for many other

applications, in the life sciences and elsewhere.

d. Medical Informatics Platform. The Medical Informatics Platform will federate genetics,imaging, and other clinical data currently locked in hospital and research archives and

make the data available to relevant research communities. An important goal will be to

use the platform to identify biological signatures of disease. Success would accelerate the

development of a new category of biologically based diagnostics, supported by strong,

mechanistic hypotheses of disease causation. Hypotheses developed in this way could

then be tested through in silico experiments on the Brain Simulation Platform . The

results will help researchers to identify new drug targets and new strategies for treatment,

providing valuable input for industry decision-makers before they invest in expensiveprogrammes of animal experimentation or human trials.

e. Neuromorphic Computing Platform. The Neuromorphic Computing Platform will allownon-expert neuroscientists and engineers to perform experiments with Neuromorphic

Computing Systems (NCS): hardware devices incorporating simplified versions of the

brain models developed by the Brain Simulation Platform , state-of-the-art electronic

component and circuit technologies as well as new knowledge arising from other areas of

HBP research (experimental neuroscience, theory). The platform will provide access to

three classes of NCS: systems based on physical (analogue or mixed-signal) emulations of

brain models (NM-PM), running much faster than real time; numerical models running

in real time on digital manycore architectures, (NM-MC), and hybrid systems. The

platform will be tightly integrated with the High Performance Computing Platform ,which will provide essential services for mapping and routing circuits to neuromorphic

substrates, benchmarking and simulation-based verification of hardware specifications.

f. The Neurorobotics Platform. The Neurorobotics Platform will offer scientists andtechnology developers a software and hardware infrastructure allowing them to connect

brain models, implemented through the Brain Simulation Platform or on neuromorphic

computing systems to detailed simulations of robot bodies and their environments, or to

physical robots. The capabilities provided by the platform will allow cognitive

neuroscientists to perform closed-loop experiments dissecting the neuronal mechanisms

responsible for specific cognitive capabilities and behaviours, and will support the

development of neurorobotic systems for applications in specific domains

(manufacturing, services, automatic vehicles etc.)

4. Applications. The HBP will support research projects that use the platforms to accelerate research inneuroscience (dissecting the biological mechanisms responsible for cognition and behaviour),

medicine (understanding brain disease, developing new diagnostic tools and treatments, personalised

medicine) and computing (novel architectures for high performance computing; low-energy

computing systems with brain-like intelligence; hybrid systems integrating neuromorphic and

conventional technologies; applications for industry, services, vehicles, and the home). The majority of

these projects will be carried out by groups from outside the current HBP Consortium, selected via

independent peer review within the HBP Competitive Calls Programme.

5. Ethics and society. The HBP will organise a large Ethics and Society Programme. The programme

will include a Foresight Lab, responsible for investigating the projects likely impact on society;

academic studies of the projects implications for beliefs about the human mind, identity, personhood,

and our capacity for control; a far reaching programme to build ethical and social awareness amongscientists working in the HBP; and a multifaceted series of initiatives to build and maintain a dialogue

with civil society. An independent Ethical, Legal and Social Aspects committee will provide


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governance and guidance on the ethical implications of the projects expected outcomes; a second

Research Ethics Committee will supervise day-to-day research practices (clinical research with human

subjects, animal experimentation etc.) ensuring compliance with all relevant legal, regulatory and

ethical requirements. As shown in Figure 4, each research area will be further divided into subprojects.

In what follows, we provide a detailed description of individual research areas and subprojects.

1.2.1 Data

Modern neuroscience research has generated vast volumes of experimental data and large-scale initiatives

launched in recent years will gather much more. Nonetheless, much of the knowledge needed to build multi-

level atlases and unifying models of the brain is still missing.

The human brain contains close to 100 billion (1011) neurons and a million billion (1015) synaptic connections,

each expressing different proteins on the cell membrane and each with its own complex internal structure.

Despite huge advances, there is no technology on the horizon that allows us to characterise more than a tiny

part of this complexity. Furthermore, obvious ethical considerations place tight constraints on the use of

invasive techniques. This means that to understand the human brain we have to maximise the information we

can extract from research in other mammalian species and from limited human datasets (e.g. data from

neuroimaging, EEG and ECOG; data from autopsied brains; data from human IPSC; genetic data). The HBPs

data generation strategy will thus focus on a small set of strategically critical datasets, for mice and humans,which could allow the project to identify principles making it possible to predict difficult-to-measure values

from data that is more readily available. EPFLs Blue Brain Project has recently published a paper

demonstrating the successful use of experimental data on neuron morphologies to predict connectivity in

neuronal microcircuits [1]. The HBP plans to systematically extend this Predictive Neuroinformatics approach,

deriving and validating general principles of brain organisation that reduce the need for direct experimental

measurements and which point to gaps in our knowledge where such measurements are indeed essential. One

of the first essential steps will be to develop methods making it possible to synthesise neuron morphologies and

electrophysiological behaviour from data on the genes expressed in particular types of cell (cell-type

transcriptomics) and to estimate the number of cells of different types in different areas of the mouse and

human brains from publicly available maps of gene expression [2, 3].

Data generation in the HBP will be divided into three subprojects dedicated respectively to the Multi- levelorganisation of the mouse brain, the Multi-level organisation of the human brain, and Brain function and

cognitive architectures.

1.2.1.1 SP1: Strategic mouse data

Operational objectives

As just described, ethical and technical considerations make it difficult to obtain data about the detailed

structure and function of the human brain. To understand the role of different levels of biological organisation,

it is essential, therefore, that we learn as much as possible from other mammals. One promising strategy is to

collect systematic data describing different levels of brain organisation in a single species and analysing how

variation in structure and function relates to genetic variation. Much of our current knowledge and methods

come from studies in mice. The goal of the HBP will thus be to build on this knowledge, generating systematicdata sets for mouse genomes and molecules, cells and circuits. The results will help to fill in gaps in the current

data and to discover general principles applicable to models of the human brain.

State of the art

Current neuroscience comprises many disciplines and research communities, each focusing on a specific level

of biological organisation, and on the brain regions, species and methods best adapted to its specific goals.

Progress is rapid at all levels. However, there are many gaps in our current knowledge. At the molecular level,

we lack a complete description of the genes expressed in single neurons or the protein composition of

synapses. At the cellular anatomy and connectivity levels, we still do not have complete data for a single

species; even in C. elegans the only animal whose neuronal circuitry has been completely deciphered

essential information, such as data on neural morphologies is still missing. At the physiological level, we do nothave a clear, quantitatively accurate picture of physiological response in different types of synapse, cell and

circuit; data on long-range connections between different brain regions is similarly sparse. Without a


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systematic programme of research in a single species, it will be extremely difficult to understand the

relationships between different levels of brain organisation for instance, the way in which a variant in a

specific gene can affect the architecture of an animals neural circuitry and its subsequent behaviour. The

species for which we have most data and the best techniques of data generation is mouse.

Although an enormous amount of work remains to be done, new technologies are making it easier to

generate data on the mouse brain, and to relate the data to data for humans. At the molecular level, we

already have a large volume of quantitative data on DNA sequences and modifications [4], RNA [5] andproteins [3, 6]. The last two years have seen the release of the first molecular-level atlas of the mouse [3]and

human brains [2]. In principle, these atlases, combined with RNA and protein profiles for different cell and

synapse types, could it possible to estimate the numbers of cells of different types in different brain regions

and to relate the data for the two species, The Human Brain Project will fully exploit these possibilities.

At higher levels of organisation, breakthroughs in scalable methods, and particularly in optogenetics [7] and

MRI are paving the way for comprehensive studies comparable to the work currently in progress in

molecular biology and proteomics. In particular, there has been considerable progress in connectomics.

Molecular tracer methods now make it possible to trace connections between different types of cells and

their synapses. Data from these studies can be correlated with results from behavioural studies, traditionally

performed in low-throughput settings, but now complemented by high-throughput methods using

touchscreen based perception and learning tasks [8]. These methods mean it is now possible to measure andcompare data from thousands of animals and compare it to data from human subjects.

Methodology

HBP work in this area will be based on a cohort of genetically diverse mouse strains expressing a range of

mutations and normal gene variants. This method will allow the project to systematically generate strategically

valuable data (DNA sequences, chromatin, mRNA and protein expression, synaptic connections and cell

structure, physiology) and to compare it against human data sets. The results will cast light on the causal

relationships between different levels of brain structure. Organisational principles derived from this work will

help the HBP to estimate parameter values for human brain models that cannot be measured experimentally.

The study will seek to answer key questions concerning the relationships among different levels of brain

organisation.1. The genome. What is the relationship between differences in gene sequence and chromatin state and

higher levels of brain organisation (gene and protein expression, distributions and densities of

different cell types, connectivity, the size of different brain regions, large-scale structure of the brain)?

How can we characterise the cascade of multi-level effects leading from genes to behaviour?

2. Gene expression. What combinations of genes are expressed in different types of cells at differentages? How do gene expression and its dynamics vary among cell types? What can we predict about

cells by reading mRNA profiles? What are the mechanisms underlying spontaneous, stimulus and

environmentally driven changes in gene expression?

3. Protein expression. What range of proteins is expressed in different types of neuron, glia andsynapse? How do qualitative and quantitative variations in protein expression affect the electrical and

pharmacological behaviour of cells? What are the molecular and cell biological principles governingthe distribution of proteins within the cell? What can we learn from these distributions?

4. Cells. How many and what types of cells are present in different regions of the brain? What are theirmorphologies? How much do they vary in number and shape? What are the relationships between

gene variants, gene expression and morphology?

5. Connectivity. How many different types of synapse are there? What are the rules governing theformation of synaptic connections between neurons of different types, and the long-term stabilisation

of these connections? How are synaptic locations chosen? What are the rules governing long-range

connections between different brain regions?

6. Electrophysiology. What are the different profiles of excitability and firing patterns of differentneuronal types? What are the different types of synaptic plasticity? What are the mechanisms

underlying diversity in synaptic transmission? How do neurons process synaptic input? What are thecharacteristic emergent behaviours of neuronal microcircuits? How do microcircuits of neurons work

together to shape the dynamics of a brain region? How do past activity, network context and


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neuromodulation affect the functional expression of neurons intrinsic properties and modulate

plasticity (metaplasticity)?

7. The neuro-vascular-glial system. How do neurons, glia and blood vessels interact? What is thedetailed architecture of the vasculature that directs blood within the brain? What is the structural

relationship among neurons, glia and vessels? How do changes in neurons alter the properties of

vessels and vice versa ?

Combined with behavioural data, these coherent, multi-level data sets will provide the fundamentalinformation needed to gain insights into the relationships between different levels of biological organisation, to

identify basic principles making it possible to predict parameter values where measurements are not available,

and to bring data and principles together in unifying models. In this way, the HBP will be able to formulate

and answer new questions. Which combination and sequence of activation of different brain regions support

different forms of behaviour? How do genes and gene expression correlate with neuron morphology and

electrophysiology? What is their relationship to cognition and behaviour? How are the building blocks of

behaviour related to one another and what is their mechanistic underpinning at the molecular, cellular and

circuit levels? What is the smallest network of neurons that can perform an isolated task? How does the

composition of a neural microcircuit affect the computational operations it performs? What is the role of single

cell types in the processing of sensory and motor information? How important is multisensory information

processing for the individual senses?

The mouse data sets collected during the ramp-up phase will be carefully planned to match data sets for

humans. For example, data on gene expression in single mouse neurons will be matched against equivalent

data for human cells induced from pluripotent stem cells. Similarly cognitive data obtained from touchscreen

behavioural testing in mouse will be matched against equivalent human data sets now a standard approach.

The HBP is aware that it can only generate a very small fraction of the data needed to build detailed models of

the brain. The goal, therefore, is to create a scaffold that can then be fleshed out with data from other groups

and from predictive neuroinformatics (see below). To pilot this approach, the HBP will collaborate with the

Allen Institute for Brain Science (http://www.alleninstitute.org/) in its on-going study of the mouse visual

system. At the structural level, this study will map the volumes of brain areas implicated in the visual system,

obtaining cell numbers and distributions, as well as data on genetically characterised cell types and on neuron

morphologies. Functionally, it will identify the role of single neurons and cell types in visual information

processing and visual perception and learning paradigms. The results will be contributed to the INCF

(www.incf.org), the GeneNetwork system (www.genenetwork.org), the Genes-to-Cognition programme

(www.genes2cognition.org ) and other internationally established databases, which will provide the HBP with

standardised data resources for theoretical studies and modelling.

Roadmap and key milestones

M30: High throughput screening of the mouse brain phase 1

Methods. Informatics tools ready for managing molecular and cellular data High throughput screening. Draft transcriptomes of major cerebellar neurons; draft mouse synapse

proteome atlas distributions and relative densities of cells in the whole brain; statistical parameters

characterizing spatial arrangements between neurons, glia and blood vessels; high-resolution synaptic

map of brain regions in a given coronal plane at the mesoscopic level (numbers of PSD95 puncta);

ultrastructural data using FIB/SEM revealing principles of neuropil organisation in multiple brain

regions

Principles. Principles of quantitative connectomics and neuronal morphologies;M60: High throughput screening of the mouse brain phase 2

Methods. Optimised workflow for depositing data in the mouse brain atlas. High throughput screening. Large-scale gene sequencing, electrophysiological and behaviour profiling;

refined generic neuronal, glial and synaptic proteomes; transcriptomes of major neuron types in

major brain regions; high throughput whole brain mapping of selected ion channels and receptors;

neuronal and glial morphologies in major brain regions; refined neuro-vascular-glial system; high

throughput tracing of single axons in major brain regions (whole brain); EM block analysis of all brainregions.


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Principles. Refined activity-dependent gene expression rules for environment-sensitive brain models;comparisons of mouse and human transcriptomes, proteomes, cellular morphologies, cell counts,

presence and dimensions of brain regions.


High throughput screening. Large-scale gene sequencing, electrophysiological and behaviour profilingcomplete; generic neuronal, glial and synaptic proteomes with data on individual variation;

transcriptomes for all neuron types in all brain regions; high throughput mapping of further selectedion channels and receptors (whole brain); neuronal and glial morphologies for all brain regions;

presence and dimensions of brain regions; high throughput tracing of single axons (whole brain); final

neuro-vascular-glial system with sufficient data on variance to constrain a vascular synthesiser; EM

block analysis of all mouse brain regions providing information on individual variations in the

statistical data on synaptic connectivity and neuronal ultrastructure.

Principles. Refined activity-dependent gene expression rules for environment-sensitive brain models;comparisons of mouse and man transcriptomes, proteomes, cellular morphologies, cell counts.


High throughput screening. High throughput mapping of further selected ion channels and receptors(whole brain); high throughput tracing of single axons (whole brain).

Principles. Comparisons of mouse and human transcriptomes, proteomes, cellular morphologies, andcell counts; presence and dimensions of brain regions.

1.2.1.2 SP2: Strategic human data


Mouse data provides many insights that also apply to the human brain. Obviously, however, the human brain

is different. It is essential therefore to compare mouse data with measurements from humans. Although ethical

considerations limit the choice of methods, recent non-invasive techniques provide new options. The HBP will

use these techniques to generate a scaffold of strategically selected data on the structure and functional

organisation of the human brain at different ages and at different levels of biological organisation (see Figure

5), which it will use to catalyse and organise contributions from outside the project, filling in the gaps with datafrom predictive neuroinformatics. The results will provide essential input for multi-level models of the human

brain and for the understanding of brain disease.

State of the art

Genetics and gene sequencing. Genetics is the method of choice for understanding genome-to-phenome

linkage at the molecular, cellular and behavioural levels. Two genetic strategies have proven particularly

valuable. The first compares the phenotypes produced by point mutations against controls; the second

examines small populations of individuals and assesses the role of endogenous genetic variation (natural

polymorphisms).

Combined with massive -omic data sets, such as ENCODE [9] and the recently released atlas of the adult

human brain transcriptome [2], these approaches make it possible to build and test complex systems modelswhere every trait, at every level and scale, can be linked to specific genes loci and regulatory sequences [6]. The

recent introduction of computerised touchscreen approaches has made it possible to compare a subset of

human cognitive functions with equivalent functions in mouse [10]. Despite the limitations of mouse models

for predicting complex behaviour and cognition in humans, comparative studies of mice and humans can

provide valuable information about putative mechanisms. Functions amenable to this approach include

attentional processing, visual and auditory memory, as well as cognitive flexibility and response inhibition.

These methods provide a valuable tool for studies of normal human genetic variation.

Human mutations as a major cause of brain disease. Studies have identified over two hundred single gene

mutations affecting human postsynaptic proteins and over a hundred and thirty brain diseases in which these

mutations are believed to play a role. Recent work suggests that mutations in regulatory sequences may also

play an important role in pathogenesis [9]. Studies of individuals with these mutations can provide usefulinsights into the way variation in specific proteins contributes to differences in cognitive, behavioural and

emotional phenotypes, while simultaneously providing valuable information on mechanisms of disease


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causation. Particularly interesting are studies on individuals who carry these mutations, but who display no

overt signs of disease.

Molecular systems biology. Molecular systems biology uses mathematical and computational methods to

understand the molecular basis of information processing in the brain. For example, multi-scalar analysis of

genomic variation data and quantitative phenotype data make it possible to map patterns of gene and protein

expression to specific neuronal and synapse types. Massive, well-structured molecular data for key brain cell

and synapse types make it possible to build rich quantitative models of higher order components synapses,cells, neuronal ensembles and brain areas and to link these models to precisely matched anatomical,

functional, and behavioural data sets, a precondition for predictive modelling.

Cataloguing cell types using transcriptomic data. Large-scale mapping of gene expression patterns in the

mouse brain [11, 12] has confirmed that morphologically distinct cells express different combinations of the

same genes. The Allen Institute is now conducting similar studies on human brain material [13]. Combined

with data from single cell transcriptomics not yet available but on the horizon this data will make it

possible to predict the cell types present in different regions of the brain. In principle, the data could also

enable prediction of the proteins present in different types of cells.

Cataloguing synapse types using proteomic data. Proteomics studies of human synapses have demonstrated

that human synapses contain over a thousand different proteins [14]. The protein composition of synapses

differs between different brain regions, different neuronal types and even along the same dendrite, andcertain patterns of synaptic protein are typical of specific cell types and brain regions [15]. Array

Tomography, a new technique, makes it possible to analyse between ten and twenty synaptic proteins,

mapping synapse diversity at the single synapse level [16]. Recently developed optogenetic methods for

labelling synaptic proteins allow rapid, highly efficient mapping of individual synapse types, characterisation

of the synapses present in different regions of the brain and identification of their role in neuronal

information processing.

Living human neurons from stem cells. It is now possible to study living human neurons derived from human

induced Pluripotent Stem Cells (iPSCs) [17]. The combination of iPSCs with developmental neurobiology has

made it possible to model human cortical function in a dish [18]. In particular, the zinc finger nuclease

technique provides a tool to generate human neurons carrying specific disease mutations [19].

Imaging. Structural and functional imaging of the living human brain provide a valuable supplement to high-

resolution data from anatomicalpost mortem studies [14]. Maps of the density of the main types of neurons in

post mortem brains can link functional imaging data to underlying brain anatomy [15]. Although results still

need to be validated, recent in vivo imaging techniques, particularly diffusion and resting state imaging, have

made it possible to map large-scale patterns of structural connectivity [16-18]. Polarised Light Imaging (PLI),

detecting the myelin surrounding axons, makes it possible to link DTI data to the microscopic level and to

verify data from in vivo experiments [19]. Intra- and subcortical connection profiles for individual areas,

obtained in this way, are likely to provide new insights into the structure and function of the brain. For the

human brain, PLI is also one of the few methods that can bridge the gap between macroscopic organisation

and more detailed knowledge about long and short fibre tracts. Given that most current information on

human brain connectivity is extrapolated from animal and developmental studies, this is a crucial step.Post mortem studies provide useful information about the distribution of different types of transmitter

receptor in different regions of the brain [20]. Receptors play a key role in neurotransmission and are highly

relevant for understanding neurological and psychiatric diseases and the effect of drugs. So far, however, most

of this work has been based on static interaction representations that do not capture the full molecular

dynamics of the nervous system. This will require Molecular Dynamics models that exploit HBP high

performance computing capabilities

Today there is evidence suggests that many neurological and psychiatric diseases (e.g., epilepsy, schizophrenia,

major depression) depend on the equilibrium among multiple receptors. Modelling and simulation provide an

essential tool for understanding these complex mechanisms.

Brain models require precise data on the cellular organisation of different brain areas (e.g. cortical layers andcolumns) and their connectivity. Recent studies have combined post mortem studies of laminar cell


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distributions with in vivo diffusion techniques to measure the distribution of cell and fibre diameters, opening

the road to in vivo studies of human cytoarchitecture and connectivity.

Methodology

The single cell transcriptome. The HBP will measure the single cell transcriptome of specific types of clinically

derived brain cells and from human iPSCs. It will then compare the data with data from mouse studies.

Combined with modelling and gene expression maps, this data will make it possible to predict aspects of brain

structure that cannot be measured experimentally.The proteome. The HBP will measure the proteins expressed in human neurons, glial cells and synapses, and

compare the results against data from mice.

Distribution of receptors. This work will map the distribution of receptors for different neurotransmitters in

different brain regions, making it possible, in principle, to model the role of neurotransmission and

neuromodulation in attention, learning, emotion, reward and sleep. Knowledge of receptor distributions will

also make it possible to model some of the effects of drugs and of neurotoxins.

Neuron morphologies. This study will characterise the morphologies of different types of neuron present in

different regions of the human brain. Combined with modelling, the results will allow the project to predict a

large proportion of the short-range connectivity between neurons, without measuring the connectivity

experimentally [1].

Neuronal architecture. Neuronal architecture differs between brain regions with respect to the density, size,

and laminar distribution of cells, and the presence of cell clusters. Significant differences have been observed in

primary vs. secondary, visual vs. auditory, sensory vs. motor, and phylogenetically old vs. younger areas. This

work will map the architectures of different layers and areas of the brain, providing constraints for simulation

and modelling by introducing area-specific information on the level of large cognitive systems and behaviour.

Human brain connectomics. The HBP will use Diffusor Tensor Imaging (DTI) and Polarised Light Imaging

(PLI) to derive patterns of connectivity between brain regions and to identify fibre tracts connecting layers and

cells within brain regions. This data is essential for modelling the large-scale structural architecture of the

brain.

Mapping of the developing, adult and aging brain. Structural and functional MRI will make it possible to mapinter-individual differences in the adult human brain, and to identify structural changes characteristic of

different stages of development and aging. Such information is necessary, among other reasons, to understand

and model the formation of fibre tracts, the development of human cognition and the transition to disease.


M30. High throughput screening of the human brain phase 1

Methods. Method for alignment of DTI and PLI data established, informatics methods and toolsrefined and validated

High throughput screening. First set of high-resolution anatomical, diffusion, and functional MRIimages from the ten subjects selected for massive mapping, first quantitative description of the major

fibre tracts connecting human brain regions; quantification of T1 and T2 relaxation time in eachmajor tract of the atlas;

M60: High throughput screening of the human brain phase 2

Methods. Optimised work flow for depositing data in the human brain atlas. High throughput screening. Initial generic neuronal, glial and synaptic proteomes; transcriptomes of

human neurons using iPSCs; high throughput mapping of selected receptors (whole brain); neuronal

and glial morphologies in major brain regions; initial human neuro-vascular-glial system; EM block

analysis of selected major brain regions providing statistical data on synaptic connectivity and

neuronal ultrastructure; fMRI and DTI mapping of developing, adult and aging brains using

standardised stimulus protocols to determine macrostructure connectivity, and patterns of activation.

Principles. Initial activity-dependent gene expression rules for environment-sensitive brain modelsusing iPSCs; comparisons of mouse and human transcriptomes, proteomes, cellular morphologies,

cell counts; comparison of presence and dimensions of brain regions.


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M90: High throughput screening of the human brain phase 3

High throughput screening. Refinement and continuation of screening from phase 2. Principles. Activity- and environment-sensitive gene expression rules, transformations from mouse to

human data.


High throughput screening. Refinement and continuation of screening from phase 3. Principles. Full spectrum data for predictive reverse engineering algorithms.

1.2.1.3 SP3: Cognitive architectures


The goal of this subproject will be to use well-defined cognitive tasks, already partially studied by cognitive

neuroscience, to dissect associated patterns of brain activation and response dynamics, and to extract

principles of cognitive architecture that can be ultimately transferred into neuronal models. Studies will span

scales ranging from global networks to local cortical maps and, where possible, sets of individual neurons. The

results will allow the project to develop high-level models of the cognitive architectures implicated in particular

competencies. Combined with behavioural performance data, they will provide benchmarks for the validation

of the detailed brain models produced by the Brain Simulation Platform and guide the development ofsimplified models for use in neuromorphic devices and neurorobotic systems.

State of the art

between brain structure and cognitive function came from post mortem neuro-anatomy, the recent neuro-

imaging revolution has greatly refined our understanding of cortical and subcortical functional specialisation

[21]. Thanks to these techniques, we now have relatively precise information about the areas of the human

brain responsible for processing particular categories of visual information (e.g. information on faces, body

parts, words), so-called core knowledge systems (systems handling information about space, time or number),

language processing, and representing other peoples minds (theory of mind).

Neural codes. The localisation of the areas responsible for specific functions is a means, not an end. Recent

studies have thus attempted to characterise areas and regions in functional terms, studying how activationvaries with stimuli and tasks, and attempting to understand internal coding principles. Although the neural

basis of fMRI is not yet fully understood, high-resolution fMRI, repetition suppression and multivariate

analyses of activation patterns form an essential toolkit that, in the best cases, allows precise inferences about

the underlying neuronal codes [22, 23]. Although these codes vary across brain areas and cognitive domains,

the hierarchical Bayesian perspective emerges as a cross-domain unifying principle: neuronal populations act

as statistical predictive-coding devices that represent priors, sensory evidence, and posterior probabilities and

use them to infer and anticipate upon external events.

Spontaneous activity. Further insights come from studies of the way functional activity changes over time,

including resting state studies, in which brain activity fluctuates spontaneously [24]. While some scientists

see these fluctuations as nothing more than a consequence of neural noise in a non-random structural

network, others interpret them as memory traces in a dynamic internal model of the environment [25]. Whatis certain is that continuous but irregular spontaneous activity is a key characteristic of the brain that

distinguishes it from engineered information processing systems. Understanding resting states and their

dynamics could provide a strategy for systematically parsing functional brain areas and circuits in the living

human brain.

Neurophysiological dynamics. Timing information from fMRI has made it possible to parse the dynamics of

language and executive networks at ~200 millisecond resolution [26, 27]. Electrophysiological recordings,

using non-invasive MEG and EEG in healthy human subjects, invasive intracranial grids and single-electrode

recordings in epilepsy patients, and grids and multi-electrodes in non-human primates provide an even higher

level of spatio-temporal detail. This work has made it possible to characterise the neural codes for high-level

vision and decision-making, and to identify specialized neurons in human neocortex cells that respond

selectively to particular objects, faces, places, and people or that encode specific action goals independently ofmotor plans. Similar techniques have thrown new light on attention. A prominent proposal suggests that

attentional filtering is implemented by selective synchronisation among neurons representing behaviourally


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relevant information [28]. Recordings of local field potentials (LFP) and recordings from multiple neurons

with MEAs have made it possible to test this proposal [29].

High-level cognitive functions. The recent literature includes descriptions of networks for language

comprehension, reading, and mathematics, and their development from infancy to adulthood. Other studies

have focused on the way humans and other primates form strategies, detect errors and switch between tasks.

This work has shown how networks crossing the prefrontal and parietal regions implement a central

executive system, also called the global neuronal workspace or multiple-demand system [30].Capabilities unique to the human brain. Comparative studies often pose the question of which cognitive

abilities, if any, are unique to humans [31, 32]. Recent work shows that, at the sensory-motor level, humans

and other primates are highly similar in many respects[33, 34]. However, humans appear to be distinguished

by their recursive combinatorial ability, the capacity to bind words or other mental objects into hierarchical

nested or constituent structures, as seen in sentence formation, music and mathematics. Monkeys can perform

elementary arithmetic operations similarly to humans, and even acquire symbols for digits [35] but apparently

only humans can chain multiple operations into nested algorithms [32]. Recent studies have identified

neuronal networks associated with these capabilities [see for example 36, 37]. Finally, the human brain may

have a unique ability to represent an individuals own mind (second order or meta cognition) and the

thoughts of others (theory of mind). fMRI studies have identified a reproducible social brain network, active

during theory of mind, but also during self-oriented reflections and the resting state. Interestingly, this networkis modified in autism [38, 39].

Methodology

The HBP will combine human data from fMRI, DTI, EEG and other non-invasive techniques to provide a

comprehensive spatial and temporal description of neuronal circuits implicated in specific well-characterised

cognitive tasks. The project will focus on the following functions.

Perception-action. Invariant visual recognition; mapping of perceptions to actions; representation ofaction meaning; multisensory perception of the body and the sense of self.

Multimodal sensory-motor integration. Integration of data from vision, audition, bodyrepresentations and motor output.

Motivation, decision and reward. Decision-making; estimating confidence in decision and errorcorrection; motivation, emotions and reward; goal-oriented behaviour. Learning and memory. Memory for skills and habits (procedural memory); memory for facts and

events (episodic memory); working memory.

Core knowledge of space, time and numbers. Fundamental circuits for spatial navigation and spatialmemory; estimation and storage of duration, size and numbers of objects.

Capabilities characteristic of the human brain. Processing nested structures in language and inother domains (music, mathematics, action); generating and manipulating symbols; creating and

processing representations of the self in relation to others.

Architectures supporting conscious processing. Brain networks enabling the extraction andmaintenance of relevant information; representations of self-related information, including body

states, decision confidence, and autobiographical knowledge.In each case, the HBP will develop highly structured, easily reproducible experimental paradigms, (localiser

tasks) applicable initially in human adults, but ultimately also in infants. The study will use a variety of

techniques including high-field MRI for the creation of high-resolution activity maps, M/EEG data for the

reconstruction of neural dynamics as well as intracranial electro-corticogram (ECOG) and single-cell

recordings in epilepsy patients.

To provide a comprehensive view of the circuitry implementing these functions in individual brains, the

project will recruit a small group of subjects for repetitive scanning (10-20 scanning sessions over three

months, repeated every year). MRI scans at 3, 7 and ultimately 11.7 Tesla will make it possible to characterise

the geometrical relationships between the areas involved in each individual subject and their relationship to

anatomy and connectivity. Results will be deposited in the HBP Brain Atlas and Brainpedia.

Work will begin during the ramp-up phase. First, the NeuroSpin centre will set up an environment that makes

it possible to scan about ten individual human brains, year after year for the duration of the HBP. To do this,


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the centre will address ethical and organisational issues, define recruitment criteria and disclosure policies,

scan a primary pool of ~fifty volunteers; selecting ten, and run a first set of high-resolution anatomical,

diffusion, and functional MRI protocols with these subjects. Meanwhile, a coordinated network of HBP teams

will launch research into a subset of specific cognitive tasks. Work during the ramp-up phase will be dedicated

to the definition of experimental protocols allowing accurate parsing of the relevant cortical and subcortical

brain networks in individual subjects. The main results will thus be experimental protocol analysis tools and

pilot charts of the relevant brain system. In two cases (perception of actions and spatial navigation), where the

generation of neuroimaging and neurophysiological data is already advanced, the HBP will propose workingsimulation models of the neuronal circuitry involved.

Following this initial work, demonstrating the feasibility of the approach, the team will begin studies of the full

range of cognitive functions covered by the project work plan, using fMRI, MEG, EEG, and intracranial

recordings to investigate principles of circuit-level organisation and neuronal coding. The team will then go on

to develop simple experimental paradigms, which will first be validated within the relevant HBP team, then

transferred to the NeuroSpin facility where they will be run on the reference set of ten human subjects.

To investigate the neurophysiological mechanisms underlying these functions, the team will combine

systematic recordings of local-field potentials with single-neuron recordings in human subjects (subjects

undergoing clinical investigations of epilepsy). Parallel investigations in non-human animal models, using

multi-scale electrophysiological recording and brain-imaging, will make it possible to identify genericprinciples of cortical organisation that are common to human and non-human brains. For instance we

hypothesise that in many species, the layered structure of cortex implements a Bayesian statistical computation

for perception, multi-modal sensory integration, spatial navigation, and abstract extraction of meaning

features. In the second phase of the project, the team will study these functions in a broad range of species,

where the layer structure of cortex is simpler. We expect that these investigations will lead to the identification

of computational primitives that are conserved in evolution and of others that are highly specific to the human

brain (e.g. the structures underlying language, the use of symbols and social representations).

Finally, these neuro-cognitive observations will be synthesised into high-level neuronal models, taking their

spatial layout and connectivity from actual anatomical observations, and integrating additional principles of

cognitive coding specific to each area, and cognitive computations permitted by intra- and inter-areal

computations between subpopulations of neurons. This modelling research will be performed in closecollaboration with the Theory and the Brain Simulation subprojects.


M30: Cognitive Architectures 1

Localisers. Standardised localisers for perception-action, space, time, numbers,motivation,decision and reward, multimodal perception, learning and memory, uniquely human

capabilities

Cognitive circuits. Cognitive architectures v1 based on results from localizer experiments Model preparation. Strategies for neuromorphic implementation of models Data Management. Strategy for management of data and registration in Human Brain Atlas


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Experiments. Experiments to characterise cognitive architectures for perception-action, multimodalperception, space, time and numbers

Cognitive circuits. Cognitive architectures v2 - based on results from localiser experiments Model construction. Targeted experiments enabling the construction of models of perception-action,

multimodal perception, space, time and numbers

Data management. Registration of data in the Human Brain Atlas

Principles of cognition. Principles of perception-action, multimodal perception, space, time andnumbers


Experiments. Experiments to characterise cognitive architectures for motivation, decision and reward, learning and memory; first results from longitudinal study of human subjects Cognitive circuits. Cognitive architectures v2 - based on results from localiser experiments Model construction. Targeted experiments enabling the construction of models of motivation, decision and reward, learning and memory Data management. Registration of data in the Human Brain Atlas

Principles of cognition. Principles of motivation, decision and reward, learning and memoryM120: Cognitive architectures 4

Stimulus application. Experiments to characterise cognitive architectures for uniquely humancapabilities (symbolic representation, recursive structures and language); first results on longitudinal

study on a consistent group of human subjects

Cognitive circuits. Cognitive architectures v3 - based on results from localiser experiments Model construction. First models of language Data management. Registration of data in the Human Brain Atlas. Principles of cognition. Uniquely human capabilities (symbolic representation, recursive structures,

and language)

1.2.1.4 SP4: Mathematical and theoretical foundations of brain research


Theoretical insights from mathematics can make a valuable contribution to many different areas of HBP

research, from modelling of low-level biological processes, to the analysis of large-scale patterns of brain

activity and the formalisation of new paradigms of computation. The HBP will thus include a cohesive

programme of theoretical research addressing strategically selected themes essential to the goals of the project:

mathematical techniques to produce simplified models of complex brain structures and dynamics; rules

linking learning and memory to synaptic plasticity; large-scale models creating a bridge between high-level

behavioural and imaging data; and mathematical descriptions of neural computation at different levels of brain

organisation. In addition to the plannable, cohesive theory program implemented by the HBP partners, brain

theory also needs to explore unconventional ideas, which are most likely to come from outside the currentHBP Consortium. To foster interaction with outside scientists, the HBP will therefore establish a European

Institute for Theoretical Neuroscience, which will provide a home for HBP postdocs to work, as well as attract

theoreticians not presently involved in the project, and act as an incubator for approaches that challenge

traditional wisdom. This work will be funded under the HBP Competitive Calls Programme

State of the art

Understood as mathematical modelling, theoretical neuroscience has a history of at least a hundred years. In

general, theoreticians have focused on models addressing specific levels of brain organisation, for instance, the

relation of Hebbian learning to cortical development [40], the recall of associative memories [41],the link of

temporal codes and Spike Timing-Dependent Plasticity [42] and the dynamics of neuronal networks with

balanced excitation and inhibition [43, 44]. In most cases, the output has consisted of toy models amenable

to mathematical analysis and to simulation on small personal computers. What is not clear is how to connectthe insights from these models, or how to ground them in detailed biophysical observations.


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These are key themes in the work of the theoretical neuroscientists who have contributed to the preparation of

the HBP proposal. For example W. Gerstner has shown how to extract parameters for simple neuron models

directly from experimental data, and from detailed biophysical models [45, 46]. M. Tsodyks, W. Gerstner, N.

Brunel, A. Destexhe, and W. Senn have produced models of synaptic plasticity suitable for integration in

models of large-scale neuronal circuitry [47-50]; W. Gerstner, D. Wierstra, and W. Maass have explored

models in which plasticity is modulated by a reward signal [11, 12, 51], a basic requirement for so-called

reinforcement learning. N. Brunel has produced models of population dynamics using networks of randomly

connected simple neurons [44], an approach exploited by G. Deco to construct models of decision-making[52]. A. Destexhe [53, 54]has investigated the integrative properties of neurons and networks, while W. Maass

has studied their underlying computational principles [10, 13].

Methodology

Theoretical work in the HBP will address a broad range of issues, all related to the goal of achieving a multi-

level understanding of the brain.

1. Bridging scales. Studies will establish mathematical principles making it possible to derive simplifiedmodels of neurons and neuronal circuits from more detailed biophysical and morphological models,

population models and mean field models from simplified neuron models, and brain region models

from models of interacting neuronal populations. Other studies will model brain signals at various

scales from intracellular signals to local field potentials, VSD, EEG and MEG. The results, validated bycomparison with results from the subproject on Brain Function and Cognitive Architectures, will

provide basic insights into the relationships between different levels of brain organisation, helping to

choose parameter values for large-scale modelling, and guiding the simplification of models for

implementation in neuromorphic technology.

2. Synaptic plasticity, learning and memory. This work will develop learning rules for unsupervised andgoal-oriented learning. Key themes will include the derivation of learning rules from biophysical

synapse models, the identification of rules for unsupervised learning and emergent connectivity, rules

describing the role of neuromodulation in learning (the role of reward, surprise and novelty), and the

functional and medical consequences of disturbances in plasticity on different time scales. Theoretical

results will be validated against experimental results from the subproject on Brain Function and

Cognitive Architectures.3. Large-scale brain models. The HBP will develop simplified large-scale models of specific cognitive

functions. These models will provide a bridge between high-level behavioural and imaging data and

detailed multi-level models of brain physiology. Topics for modelling will include perception-action,

multi-sensory perception, working memory, spatial navigation, reward systems, decision-making and

the sleep/wakefulness cycle. These models will make a direct contribution to the design of

architectures for neuromorphic computing systems.

4. Principles of brain computation. Studies in this area will develop mathematical descriptions of neuralcomputation at the single neuron, neural microcircuit and higher levels of brain organisation. The

results will provide basic insights into the multi-level organisation of the brain, while simultaneously

contributing to the high-level design of neuromorphic systems.

5.

To encourage collaboration among theoreticians engaged in different areas of theoreticalneuroscience, we propose that the HBP creates a European Institute for Theoretical Neuroscience

based in the Paris area. The Institute will run active young researcher, young investigator and visiting

scientists programmes and will serve as an attractive meeting point for workshops on topics related to

HBP goals.


M30: Theoretical Foundations 1

Bridging scales. From morphologically and biophysically detailed neurons to point neurons; firstdraft principles, algorithms and models for representing neural signals deposited in Human BrainAtlas

Learning and memory. Impact of learning rules on neural circuitry. first draft principles,algorithms and models for representing neural signals deposited in Human Brain Atlas


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Large-scale models. Theoretical approach to building large-scale models using data fromcognition; first draft principles, algorithms and models of perception-action, working memory, andattention, wakefulness, and sleep deposited in Human Brain Atlas.

Principles of brain computation. Stochastic computing in neurons and circuits; methods toimplement stochastic computing in neuromorphic computing systems

EITN: Institute set up and in operation. Visiting scientists programme in operation. First two series ofworkshops completed.


Bridging scales. From molecular detailed neurons to point neurons; from point neurons to mean fieldequations; from mean field equations to conceptual models; model for representing neural signals at

different scales

Learning and memory. Detailed understanding of learning rules under the influence ofneuromodulators, in particular motivation and reward, usable for large-scale models

Large-scale models. Models of perception-action, multimodal perception, representation of space,time and numbers in brain theories and neuronal models (see also milestones p. 28)

Principles of brain computation. Stochastic and liquid computing computational paradigms, whichcan be translated into neuromorphic implementations

M90: Theoretical Foundations 3 Bridging scales. From molecular and biophysically detailed models of synaptic transmission and

plasticity to phenomenological models and learning algorithms

Learning and memory. Multi-level synaptic and circuit learning rules related to goal-oriented learningand memory; theoretical predictions of functional consequences of plasticity for selected human brain

diseases

Large-scale models. Models with motivation and reward for learning, decision making and memory(see also milestones p. 28); memory consolidation during sleep

Principles of brain computation. Stochastic computing with self-referential learning in recursivesystems and their neuromorphic implementation; principles of brain computation and information

routing at the neuronal, circuit and brain areas level


Bridging scales. A comprehensive theory relating neuronal information and signals at different scales,from neurons to brain areas

Learning and memory. A comprehensive theory of learning from synapses to memory includingprinciples of autonomously learning systems

Large-scale models. A comprehensive theory of the interaction between different brain states, fromwakefulness to sleep and systems models for (i) spatial navigation (ii) working memory (iii) goal-

oriented behaviour

Principles of brain computations. A comprehensive theory of brain computation from the singleneuron to the whole brain level, including symbolic structures and recursive representations (see also

milestones p. 28)

1.2.2 ICT platforms

The HBPs first strategic goal will be to develop and operate an integrated system of six ICT platforms,

dedicated to Neuroinformatics, Brain Simulation, Medical Informatics, High Performance Computing,

Neuromorphic Computing, and Neurorobotics, respectively. Each of these platforms is associated with a

distinct subproject in the HBP work plan. Each platform will provide services to the other platforms. To cite

just one example, the Neurorobotics Platform, will use brain models developed by the Brain Simulation

Platform, high performance computing capabilities provided by the High Performance Computing Platform

and Neuromorphic Computing systems provided by the Neuromorphic Computing Platform.

The HBP will use the platforms to support its own research and to provide high quality, profession- ally

managed services for the scientific community. These will take the form of research projects solicited through aprogramme of Competitive Calls evaluated by independent peer review . The ultimate goal is to provide a new

ICT foundation for future neuroscience, future medicine and future computing.


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1.2.2.1 SP5: Neuroinformatics Platform

Operational Objectives

One of the HBPs most important objectives is to make it easier for neuroscientists to organise and access the

massive volumes of heterogeneous data, knowledge and tools produced by the international neuroscience

community - a goal it shares with the INCF [14] and with other on-going projects in particular the Allen

Institutes Brain Atlas projects (www.brain-map.org). The Neuroinformatics Platform will contribute to these

efforts, offering new tools for the analysis and interpretation of large volumes of structural and functional dataand for the construction of multi-level brain atlases. The HBP will use these tools to develop detailed multi-

level atlases of the rodent and human brains, bringing together data from the literature, and from on-going

research, and providing a single source of annotated, high quality data for the HBP modelling effort and for the

international neuroscience community.

Another key feature of the platform will be support for Predictive Neuroinformatics: the mining of large

volumes of data and analysis of activity data to identify patterns and relationships between data from different

levels of biological organisation, making it possible to predict parameters where experimental data is not yet

available and to test and calibrate model implementations. Systematic application of this strategy has the

potential to drastically increase the amount of information that can be extracted from experimental data,

rapidly filling gaps in our current knowledge and accelerating the generation of data required for brain

modelling.

State of the art

Virtually all areas of modern science face the challenge of providing uniform access to large volumes of diverse

data. In neuroscience, with its broad range of experimental techniques, and many different kinds of data, the

challenge is particularly severe. Nearly a hundred years of research has generated a vast amount of knowledge

and data, spread across thousands of journals. The challenge now is to provide uniform access to this data.

The first attempts to achieve this goal date back to 1989, when the Institute of Medicine at the US National

Academy of Sciences received funding to examine how to handle the growing volume and diversity of

neuroscientific data. The study report, published in 1991 [15] enabled NIMH to create its own Human Brain

Project, an effort that lasted until 2004. The work produced many important neuroscience databases. However,

it never created a standard interface for accessing the data and provided no specific tools for relating andintegrating the data.

Soon after the end of the NIMH project, the OECD Global Science Forum initiated the INCF [16].Since 2005,

the INCF has driven international efforts to develop neuroscience ontologies, brain atlases, model descriptions

and data sharing, and has played an important role in coordinating international neuroscience research and

setting up standards. Other important initiatives such as the US-based Neuroscience Information Framework

(NIF) [17], and the Biomedical Informatics Research Network (BIRN) [18] are now collaborating closely with

INCF on issues related to infrastructure, brain atlases, ontologies and data sharing. Another important

organisation, working in this area, is the Allen Institute, today a world leader in industrial-scale acquisition of

neuroscience data. The Allen Institute has developed mouse and human atlases for gene expression,

connectivity, development, sleep and the spinal cord, and has recently investing an additional $300M for in

vivo data acquisition and modelling related to the mouse visual system [19]. Data from this work willcontribute directly to the HBP.

A second key area of activity for the HBP will be Predictive Neuroinformatics, a relatively new area of research.

Examples of work in this area include a recently published algorithm that can synthesise a broad range of

dendritic morphologies based on sample morphologies [[20], algorithms to generate specific motifs in network

connectivity from sampled connectivity patterns [55], and algorithms to predict synaptic strength based on

results from electrophysiological studies of connectivity [56]. In related research, a recent study has

demonstrated that biophysical models of neurons electrophysiological properties can successfully predict ion

channel distributions and densities on the cell surface [57]. Combining these predictions with cellular

composition data makes it possible to predict protein maps for neural tissue. Finally, predictive

neuroinformatics can help to resolve one of the most important challenges for modern neuroscience, namely

the classification and categorisation of different types of cortical interneurons [58]. A recent model [59] uses

gene expression data to predict type, morphology and layer of origin with over 80% accuracy. The same model

reveals rules for the combinatorial expression of ion channel genes [60].


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Methodology

The HBP will build and integrate a Neuroinformatics Platform incorporating five distinct sets of components.

Tools for brain atlases. The HBP will create a general-purpose open source software framework, allowing

researchers to build and navigate multi-level atlases of the brain of any species. These tools will allow

researchers to upload and access multi-level information about any part of the brain. The information

contained in the atlases will be distributed across databases in different physical locations. The framework will

provide a shared data space, ontologies, data mining tools, standards and a generic Atlas Builder, making itpossible to build, manage and query such atlases. In addition to this work, the project will also create and

manage an HBP Brainpedia a community-driven Wiki that provides an encyclopaedic view of the latest

data, models and literature for all levels of brain organisation.

Tools to analyse data on brain structure. Much of the structural data produced by modern neuroscience takes

the form of image stacks from light and electron microscopy, MRI, PET etc. Given that many of these

techniques produce terabytes of data in a single session, the best way to unlock the information they contain is

through automatic image processing. The HBP will develop tools for this purpose, which it will share with the

community, via the INCF. These will include software to automate the extraction of cell densities and

distributions; the reconstruction of neuron morphologies; the determination of subcellular properties such as

synapse and organelle geometry, size and location; and the identification of the long-range fibre tracts

underlying connectivity.Tools to analyse data on brain function.Understanding of brain function depends on data from a wide range of

techniques. It is important that simulation results should be comparable against this data. To meet this need,

the HBP will develop new tools and techniques to compare data from simulations against data from

experiments (single neuron recordings, measurement of local field potentials, EEG, fMRI, MEG etc.). Some of

these tools will build on previous work in the BrainScaleS project.

Predictive neuroinformatics.The HBP will make a major effort to develop new tools for predictive informatics,

using machine learning and statistical modelling techniques to extract rules describing the relationships

between data sets for different levels of brain organisation. An important goal will be to predict neuron

morphology and electrophysiology from data on gene expression in different types of neuron.

Brain atlases. The HBP will use the tools just described to build multi-level atlases of the mouse and humanbrains. The design will encourage research groups outside the project to deposit data in the atlases, enabling

global collaboration to integrate data across scales in a single atlas for each species.

Roadmap and milestones

M30: Collaborative platform for databasing the brain (Neuroinformatics Platform v1)

Access and user services. Web site; guidebook Brainpedia and Atlas tools. Common dataspace, ontologies, and standardised workflows; spatial

referencing of data; drag and drop interface for depositing data; integration of genomic andanatomical data; advanced query capabilities using techniques developed in the HPC sub-project, Analysis capabilities for EM segmentation and LFP analysis (spike sorting); functionalanalysis capabilities for spike sorting and LFP analysis;

Brainpedia. Brainpedia v1 (initial data on mouse ion channels, neuron types, andmicrocircuitry)

Atlases. First draft of a navigable 3D mouse atlas. First draft of a navigable human 3Datlas

Predictive reverse engineering. First draft algorithm to identify brain regions targeted by axonalprojections

M60: Internet accessible 3D mouse brain atlas and encyclopaedia (Neuroinformatics Platform v2)

Access and user services. Community measures to drive deposition of data (recognition forresearchers, agreements with journals, editing rights, free access to analysis and visualisation

capabilities); updated guidebook

Brainpedia and atlas tools. Cross-species links between mouse and human atlases; semantic search;spatial and temporal registration tools; automated search and pull for new data and literature;

automated author notifications


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Brainpedia. Brainpedia v2 (all levels of brain organisation; community driven maintenance and auto-aggregation of global content.

Atlase

Vision Document HBP.pdf

Documents

Transcript of Vision Document HBP.pdf