The Human Brain Project

A Report to the European Commission
Šī ziņojuma autori raksta, ka par smadzeņu pētīšanas tēmu ir sarakstīts un publicēts tūkstošiem darbu, un neviens pētnieks tos vairs nevar izlasīt. Citiem vārdiem, pateikts tas pats, ko grāmatā ‘Universe in a Nutshell’ rakstīja Stīvens Houkings: “90% from everything published in science is trash”.
HBP mērķis ir mēģināt apvienot derīgo paveikto, apzināt faktisko stāvokli (derīgās zināšanas un paveikto) un izpildīt jaunus pētījumus. 
Understanding the human brain is one of the greatest challenges facing 21st century science. If we can rise to the challenge, we can gain fundamental insights into what it means to be human, develop new treatments for brain diseases, and build revolutionary new Information and Communications Technologies (ICT). In this report, we argue that the convergence between ICT and biology has reached a point at which it can turn this dream into reality. It was this realization that motivated the authors to launch the Human Brain Project – Preparatory Study (HBP-PS) – a one year EU-funded Coordinating Action in which nearly three hundred experts in neuroscience, medicine and computing came together to develop a new “ICT-accelerated” vision for brain research and its applications. Here, we present the conclusions of our work.
We find that the major obstacle that hinders our understanding the brain is the fragmentation of brain research and the data it produces. Modern neuroscience has been enormously productive but unsystematic. The data it produces describes different levels of biological organisation, in different areas of the brain in different species, at different stages of development. Today we urgently need to integrate this data – to show how the parts fit together in a single multilevel system.
Robotu pētīšana ir iecerēta divos līmeņos. Lai atstrādātu programmu, virtuālu robotu ievieto virtuālā ārējā vidē, un tajā noeksperimentē tā ‘izturēšanos’ un optimizē programmas. Pēc tam optimizēto programmu ievieto fiziskā robotā un tam liek darboties reālā vidē.
State of the art
The evolutionary function of a brain is to control organisms’ behaviour in their environment. In principle, therefore, the only way to test or characterize the high level behavioural or cognitive capabilities of a brain model is to create a closed loop between the model and a body acting in an environment and to interrogate the model through well-designed experiments (see Figure 28). Once a set-up has successfully replicated we can then identify causal mechanisms by lesioning or manipulating specific brain regions, transmitter systems, types of neuron etc.
Although robotics has yet to win broad recognition as a valid tool for cognitive and behavioural research, a number of groups have attempted to use robots as an experimental tool. Current work can be roughly divided into models based on ideas, models driven exclusively by behavioural data and models that combine biological and behavioural data. It is this last category of model, which is most relevant to the HBP.
An initial strategy for closed-loop experiments in the HBP could be defined as follows.
1. Researchers would choose a cognitive or behavioural capability that has already been well characterized in cognitive and behavioural studies and for which theory has already identified a putative cognitive architecture. They would then design an in silico experiment to test the ability of a model brain to reproduce this capability and to dissect the multi-level mechanisms responsible. The experimental design would be comparable to the design for an animal or human experiment.
2. They would then use the Neurorobotics Platform to design a simulated robot body and a simulated environment, linking the body to a brain model on the High
Performance Computing Platform, chosen to represent an appropriate level of biological detail, for instance a biologically detailed model for a study of a drug, a point
neuron network for a study of the neuronal circuitry responsible for a particular behaviour.
3. Once the brain model was established, the platform would export a simplified version to a physical emulation of the model running on neuromorphic hardware.
The platform would provide the interface to couple the neuromorphic device to the simulated robot and environment. The new set-up would run many times faster
 than real-time, making it possible to train the model over long periods of simulated time.
4. Once trained, the model would be tested, comparing the results against animal or human studies. Quantitative and qualitative differences would be analysed, and the results used to refine the brain model, the robot body and the training protocol.
5. Once the model displayed the desired cognitive or behavioural capability, researchers would dissect the underlying neural mechanisms, performing manipulations
(e.g. systematic lesions, systematic changes in neuronal morphology or in synaptic transmission) and making systematic measurements (e.g. measurements
of cell activity and synaptic dynamics), impossible in animals or in human subjects. These methods should make it possible to obtain new insights into the neuronal
circuitry responsible for the model’s capabilities, confirming or disconfirming theoretical hypotheses, and guiding the development of technologies inspired by these insights.
6. Where appropriate, the trained brain model would be exported to digital neuromorphic devices allowing physical robots to perform the experimental task in real
time, in a physical environment. Such physical robots would provide a starting point for future applications (see below).
Pilot studies and open calls should encourage experimental investigations of a broad range of perceptual, cognitive and motor capabilities, beginning with capabilities
that are relatively simple and gradually moving towards more advanced functionality. Candidate capabilities could include basic visual, auditory and somatosensory processing including multisensory perception; object recognition (recognition of faces, body parts, houses, words etc.); action recognition; novelty detection (e.g. auditory novelty detection through mismatch negativity); motivation, emotion and reward; premotor transformations, motor planning and execution of motor behaviour; representations of the spatial environment and navigation; decisiondecision-making and error correction; information maintenance and memory encoding: working memory, time-dependent stabilization of cortical representations; and language production and processing.
Accelerated Neuroscience
• Tools for massive data management
• Internet accessible collaborative tools
• Brain atlases and encyclopedia
• Data intensive computing tools
• Data and knowledge predictors
• In silico systems for experiments
• Closed loop technology
• Theory for bridging scales
• Multi-level view of brain function
The HBP seeks to accelerate research into the causes, diagnosis and treatment of neurological and psychiatric disease (see Figure 29). As a first step, the HBP should use the Medical Informatics Platform and the data it generates to identify biological signatures for specific disease processes, at different levels of biological organisation. This work would lead towards a new nosological classification based on predisposing factors and biological dysfunctions rather than symptoms and syndromes.
We propose that pilot projects should test this strategy for autism, depression and aging and Alzheimer’s disease. Open calls for proposals would encourage outside researchers to extend this work and to investigate other diseases. The second goal should be to use biological signatures of disease as a source of insights into disease processes and to use modelling and simulation as tools to investigate hypotheses of disease causation.
The third goal should be to use disease models to identify potential drug targets and other possible treatment strategies and to simulate their desirable and potentially adverse effects. The fourth goal should be to develop strategies for personalized medicine, allowing the development of treatment strategies adapted to the specific condition of individual patients.
State of the art
Presently there are very few neurological and psychiatric diseases whose causes are fully understood even when their patho-anatomy and patho-physiology are largely known. For example, in Parkinson’s disease we still do not understand the steps that lead from degeneration of less than a million specific nigro-striatal cells to the first clinical symptoms (tremor, akinesia), which only appear when 60% of these cells have already been lost [103]. In a small proportion of cases, the
damage is due to exogenous poisons [124]. In many cases, however, the triggering factor(s) is unknown. This situation is complicated by the fact that other relatively common brain diseases have similar Parkinsonian manifestations. It is not known why such symptoms are so common.
Information from Genome-Wide Association Studies (GWAS) has made it increasingly clear that many diseases with different biological causes (e.g., the spino-cerebellar ataxias and multiple associated mutations) present with similar symptoms and vice versa (e.g., Huntington’s disease presenting with emotional disorders, cognitive deficits or movement disorder). These relationships make it difficult to identify specific drug targets, to select patients for clinical trials and to create homogeneous trial cohorts. These are some of the reasons why many pharmaceutical companies have withdrawn from brain research.
Problems with current systems of disease classification and scientific advances – particularly in genetics – are slowly leading researchers to shift their attention from syndromic to biologically-grounded classifications of disease. Until recently, for instance, the dementias were still diagnosed in terms of dementing syndromes, which often failed to match final post mortem analyses. Today, by contrast, clinicians are beginning to interpret neurodegenerative disorders, including the dementias, as diseases of protein misfolding [125].
The Medical Informatics Platform would place Europe in a position in which it could pioneer this new biological approach to nosology.
Another area of research, of great relevance to the HBP, is simulation-based pharmacology. Current applications of simulation in drug design focus on the dynamics of molecular interactions between drugs and their targets. To date however, there has been little or no work simulating the complex cascade of events that determines desirable or adverse effects at higher levels of biological organisation. The lack of effective methods to predict these effects may be one reason for the high rate of failure of CNS drugs in clinical trials. Recent pharmacogenetic studies of anticonvulsants (patient responsiveness to positive drug effects and predisposition to adverse effects) support this hypothesis [126].
Categorizing human brain diseases. A first important goal for the HBP should be to identify specific biological signatures that characterize disease processes. The discovery of such signatures would result in a new nosology, based on objective and reproducible biological and clinical data such as brain scans of various types, electrophysiology, electroencephalography, genotyping, metabolic, biochemical and haematological profiles and validated clinical instruments providing quantitative measurements of emotion and behaviour.
Initial work by the HBP should focus on the biologically grounded categorization of autism, depression and Alzheimer’s. However, a large part of the overall budget
should be reserved for open calls, encouraging research by scientists from outside the Consortium. The calls should encourage systematic study of the full range of neurological and psychiatric disease, making no distinction between disorders of perception, cognition, action, mood, emotion and behaviour.
Simulate hypotheses of disease causation. The discovery of biological signatures for a disease would suggest hypotheses of disease causation. The Brain Simulation Platform should therefore allow researchers to model alterations in brain physiology and structure they believe to be implicated in different diseases and to simulate the complex non-linear interactions leading to changes in cognition and behaviour. The realization that the brain is not susceptible to linear analysis has come slowly.
Again, we are at a tipping point – the Brain Simulation Platform would make it possible to simulate the effects of brain lesions on the overall functioning of brain systems, including the short-term, adaptive plasticity effects that normally palliate lesions. Simulation would also facilitate the testing of causative hypotheses for diseases for which there are no available animal models, and for disorders where such models are inadequate, for example, when disorders are associated with defects in higher cognitive function. Simulation would also teach researchers to distinguish between causative and secondary alterations associated with disease processes. The success of this kind of research will be judged by its contribution to understanding the role of different levels of biological organisation in brain disease, and to identifying new targets for treatment.
The brain evolved to control the body as it interacts with its environment, interactions that many researchers in robotics have attempted to replicate. In most cases, however, the models and robots they have used have been distant from biology. The HBP Neurorobotics Platform would allow them to interface a detailed brain model to a simulated body with an appropriate set of actuators and sensors, place the body in a simulated environment, train it to acquire a certain capability or set of capabilities, and compare its performance against results from human or animal experiments (see Figure 14). The platform would provide the tools and workflows necessary to perform this kind of experiment and to dissect the cognitive architectures involved, enabling radically new strategies for studying the multi-level mechanisms underlying behaviour. Additional tools for technology developers would make it possible to transfer brain models developed in simulation to physical robots, with low power neuromorphic controllers – an essential step towards the development of robots – robotic vehicles etc. – for use in manufacturing, services and the home.
The HBP Neurorobotics Platform should make it easy for researchers to design simulated robot bodies, to connect these bodies to brain models, and to embed the bodies in dynamic simulated environments. The resulting set-ups should allow them perform in silico experiments, initially replicating previous experiments in animals and human subjects, but ultimately breaking new ground.
The platform should provide researchers with access to simulated brain models running slower than real time, and to emulated models running faster than real time. In initial experiments, robots and environments would be simulated. For applications-related work requiring real-time operation, the platform should provide access to main-core implementations suitable for use with physical robots and machinery, together with the necessary interfaces (see Figure 26).
The Neurorobotics Platform should consist of three core modules.
Simulated robots
This module would allow researchers to build simulated robots based to detailed specifications. It would include the following components.
• A Robot Builder: a generic tool to design, develop and deploy simulated robots.
• A Sensory System Builder: a tool to generate models of perception in different modalities (auditory perception, visual perception etc.).
• A Motor System Builder: a tool to generate models of motor systems (muscles or motors) and of the peripheral nervous system.
• A Brain-Body Integrator: automated routines for the calibration of brain models to work with the selected body sensory and motor systems.
Simulated environments
This module would allow researchers to build rich simulated environments in which they can test their robots and run experiments. The module would provide the following tools.
• An Environment Builder: a generic software tool for designing and deploying dynamically changing simulated environments.
• An Experiment Designer: a tool to configure experiments and to specify testing and measuring protocols.
• An Electronic Coach: a software tool allowing researchers to define and execute multi-stage training protocols for robots (specification of timing, stimuli, correct and
incorrect behaviours, and reward signals for each stage).
Closed-loop engine
This module would make it possible to create a closed loop between a simulated or a physical robot and a brain model. It would include the following.
• A Closed-loop Engine: generic tools to couple software and neuromorphic brain models to simulated and physical robots and to other devices.
• The Human Interaction interface: a software tool that allows human experimenters to interact with robots and their environment.
• A Performance Monitor: a set of tools to monitor and analyse the performance of the neurorobotic system in its environment, and to produce configurable diagnostic
Building and operating the platform
The HBP Neurorobotics Platform would integrate the three modules and provide researchers with a control centre, where they could configure, execute and analyse the results of neurorobotics experiments. A dedicated team would provide users with the training support and documentation required to make effective use of the platform. The HBP should run an active visitors programme for scientists wishing to use the platform.
Vērtīgs pārskats par stāvokli nozarē un tuvākājā nākotnē iecerēto. PDF: 

About basicrulesoflife

Year 1935. Interests: Contemporary society problems, quality of life, happiness, understanding and changing ourselves - everything based on scientific evidence.
This entry was posted in Artificial Intelligence. Bookmark the permalink.

Leave a Reply

Please log in using one of these methods to post your comment: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.