The goal of the Blue Brain Project is to build biologically detailed digital reconstructions and simulations of the rodent, and ultimately the human brain. The supercomputer-based reconstructions and simulations built by the project offer a radically new approach for understanding the multilevel structure and function of the brain. The project’s novel research strategy exploits interdependencies in the experimental data to obtain dense maps of the brain, without measuring every detail of its multiple levels of organization (molecules, cells, micro-circuits, brain regions, the whole brain). This strategy allows the project to build digital reconstructions (computer models) of the brain at an unprecedented level of biological detail. Supercomputer-based simulation of their behavior turns understanding the brain into a tractable problem, providing a new tool to study the complex interactions within different levels of brain organization and to investigate the cross-level links leading from genes to cognition.
Blue Brain Project
With its 100 billion neurons (brain cells) and its 100 trillion synapses, the human brain is a complex multi-level system. The connections among the neurons form a hierarchy of circuits, from local microcircuitry up to the level of the whole brain. Meanwhile at a lower level, every neuron and every synapse is a complex molecular machine in its own right. It is the interactions between these levels, which give rise to human behaviour, human emotion and human cognition.
The Blue Brain Project (BBP) aims to build comprehensive digital reconstructions (computer models) of the brain, which include the brain’s different levels of organization and their interactions, and which are compatible with the available experimental data. The project’s current reconstructions reproduce the detailed cellular anatomy, connectivity, and electrical behaviour of a small part of the neocortex of young rats (about one third of a cubic millimetre of cortical tissue).
The BBP’s reconstruction strategy identifies interdependencies in the experimental data (e.g. dependencies between the size of neurons and neuron densities, dependencies between the shapes of neurons and the synapses they form, dependencies between the numbers of “boutons” on axons and synapse numbers) and uses them to “constrain” the reconstruction process. Multiple, intersecting constraints allow the project to build the most faithful reconstructions possible from the sparse available experimental data– avoiding the need to “measure everything”. In fact, the BBP’s latest reconstruction of the cortical microcircuit connectivity is based on experimental data representing less than 1% of the synaptic connections in the circuit. Lab experiments providing new data and constraints make it possible to test and progressively improve the digital reconstruction. Data and neuron models used in our current reconstructions are already available at the projects NeuroMicrocuitry Web Portal (https://bbp.epfl.ch/nmc-portal) and will soon be accessible through the Human Brain Project’s Brain Simulation Platform.
Digital reconstructions of brain tissue represent a snapshot of the anatomy and physiology of the brain at one moment in time. BBP simulations use mathematical models of individual neurons and synapses to compute the electrical activity of the network as it evolves over time. This requires a huge computational effort, only possible with large supercomputers. Simulations of larger volumes of tissue, at ever higher levels of detail, will need ever more powerful computing capabilities.
The BBP uses its digital reconstructions as the basis for a potentially unlimited range of simulations, each representing an in silico experiment. Researchers can measure the spontaneous electrical activity of the virtual tissue, apply stimulation protocols and measure the response, and manipulate the tissue in various ways (e.g. by “knocking out” cells with particular characteristics, by “lesioning” part of the circuit). In silico experiments can replicate past laboratory experiments – an important test for the reconstructions – or can explore new ground and suggest new experiments. In particular, simulation allows experiments that would be difficult or impossible in biological tissue or in living animals – for instance, experiments requiring simultaneous measurements from large numbers of neurons.. Such experiments provide a new tool for researchers seeking to understand the causal relationships between events at different levels of brain organization. – for example, understanding on how changes in composition of the extra-cellular fluid (which impact the activity of synapses) affect the overall pattern of activity in the virtual tissue.
As digital reconstructions become larger, more detailed and more biologically accurate, the range of experiments they enable will grow. The Blue Brain Project is currently building neurorobotics tools in which brain simulations are coupled to simulated robots and a simulated environment, in a “closed loop”. The new tools will make it possible to replicate cognitive and behavioural experiments in animals, where the animal’s sensory organs capture and encode information about its environment, and its brain generates a motor response. This approach will enable early in silico studies of the brain mechanisms underlying animal perception, cognition and behaviour.
Why is this important?
Understanding the brain is vital, not just to understand the biological mechanisms which give us our thoughts and emotions and which make us human, but for practical reasons. Understanding how the brain processes information can make a fundamental contribution to the development of new computing technology – neurorobotics and neuromorphic computing. More important still, understanding the brain is essential to understanding, diagnosing and treating brain diseases that are imposing a rapidly increasing burden on the world’s aging populations.
Even a brain that is much smaller than the human brain, like the brain of a rat, is so complex that may never be possible to exhaustively measure all its anatomical features or to fully characterize the physiological interactions within and between its different levels of organization. But this may not be necessary. The structure of the brain and the physiology of its components are subject to tight biological constraints, which are reflected in experimental measurements. The BBP exploits these interdependencies to build comprehensive digital reconstructions from the sparse experimental data that is available and to refine these reconstructions as the data improve. This ability makes the BBP approach inherently scalable.
Simulations suggest that our reconstructions can accurately reproduce many phenomena reported in previous laboratory experiments – without changing the parameters of the reconstruction. As digital reconstructions are refined, expanded, and validated for new kinds of experiment, they can become an ever more valuable resource for neuroscience research, allowing experiments and providing insights that would not be possible with alternative approaches.
The BBPs current digital reconstructions are first drafts, to be refined in future releases. The fact that they are detailed means they are “data ready” – it is easy to incorporate data from new experiments as they become available. The BBP will dedicate significant effort to this task. Current BBP reconstructions omit many features of neural anatomy and physiology that are known to play an important role in brain function. Future BBP work will enrich the reconstructions with models of the neuro-vascular glia system, neuromodulation, different forms of plasticity, gap-junctions, and couple them to neurorobotics systems, enabling in silico studies of perception, cognition and behaviour.
A second major effort will be dedicated to reconstructions and simulations on a larger scale than neural microcircuitry. The Blue Brain team is already working with communities in the Human Brain Project and beyond, to build digital reconstructions of whole brain regions (somatosensory cortex, hippocampus, cerebellum, basal ganglia) and eventually the whole mouse brain. This work will prepare the way for reconstructions of the human brain, on different scales and with different levels of detail.
Finally, a very large part of BBP activity is dedicated to engineering: developing and operating the software tools, the workflows and the supercomputing capabilities required to digitally reconstruct and simulate the brain and to analyse and visualize the results.
The Universidad Politécnica de Madrid (UPM) and Instituto Cajal (IC) from Consejo Superior de Investigaciones Científicas (CSIC) are involved in the Blue Brain Project (BBP) with an initiative named Cajal Blue Brain. Different research groups and laboratories from Spanish institutions take part in this initiative, grouping together a large number of scientist, engineers and practitioners.
A team of computational neuroscientists at the Hebrew University in Jerusalem headed by Idan Segev is working on aspects of modeling electrical properties and synaptic integration.
A collaboration with Phil Goodman of the University of Reno, Nevada (now deceased) has resulted in a general purpose simulation framework and research into characterizing and modeling electrical properties of neurons with ion channels.
Michael Hines of Yale University is the author of the NEURON simulator and is collaborating on a variety of enhancements and optimizations for the BlueGene supercomputer.
A collaboration with Alex Thomson , head of the Department of Pharmacology of The School of Pharmacy at the University of London, on experimental techniques and experimental data on Neocortical electrophysiology and anatomy is in place.
LIST OF PARTNERSHIPS
Aside from the many people involved at the EPFL itself, numerous partnerships have been forged with other institutions in the context of the Blue Brain project.
Hebrew University, Jerusalem
Idan Segev and his team at the Edmond and Lily Safra Center for Brain Sciences helped conceive neuronal models, in particular by developing a multi-objective optimization taking into account the diversity of electrical phenomena at the neuronal level.
IBM, Yorktown Heights (NY)
IBM’s T.J. Watson Research Center made its researchers available to help install the BlueGene supercomputer and set up circuits that would be adequate for simulating a neuronal network.
St. Elizabeth’s Medical Center, Boston (MA)
Yun Wang helped improve understanding of modeling the interneuronal system for neocortical inhibition, which made it possible to explain how the brain can adapt despite the use of a simple web.
Universidad Politecnica de Madrid, Madrid
The Cajal Institute, where Javier de Felipe Oroquieta works, lent its expertise especially in the field of cortical microorganization in pathological cases.
University of London, London
Through Alex Thomson and her group, the School of Pharmacy’s pharmacology department contributed to a better understanding of how neurons connect to each other within the layers in which they are found.
University of Nevada, Reno (NV)
Through Phil Goodman’s Brain Computation Lab, it was possible to develop some of the neuroinformatics tools the project required, especially for developing dynamic models of neuronal electrical activity, for example, in the rat.
Yale University, New Haven (CT)
Michael Hines of the Computer Science department helped solve problems related to the performance of large networks during the simulations, in particular using the mechanism of parallel networks.
多くの計算生物学は： 原理 → シミュレーション
ブルー・プリント・プロジェクトは： シミュレーション → 原理