Blue Brain Project

Last updated: Aug 14, 2012

The Blue Brain Project is an attempt to reverse engineer the human brain and recreate it at the cellular level inside a computer simulation. The project was founded in May 2005 by Henry Markram at the EPFL in Lausanne, Switzerland. Goals of the project are to gain a complete understanding of the brain and to enable better and faster development of brain disease treatments.

The research involves studying slices of living brain tissue using microscopes and patch clamp electrodes. Data is collected about all the many different neuron types. This data is used to build biologically realistic models of neurons and networks of neurons in the cerebral cortex. The simulations are carried out on a Blue Gene supercomputer built by IBM. Hence the name "Blue Brain". The simulation software is based around Michael Hines's NEURON, together with other custom-built components.

As of August 2012 the largest simulations are of mesocircuits containing around 100 cortical columns (image above right). Such simulations involve approximately 1 million neurons and 1 billion synapses. This is about the same scale as that of a honey bee brain. It is hoped that a rat brain neocortical simulation (~21 million neurons) will be achieved by the end of 2014. A full human brain simulation (86 billion neurons) should be possible by 2023 provided sufficient funding is received.

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Latest news

July 9, 2012  -   The FET Flagship Pilots final conference took place in Brussels today. Results of the recently-completed one-year pilot phase of the Human Brain Project (HBP) were presented. See the 108-page HBP report, as well as the conference statement by EC vice-president Neelie Kroes. During autumn 2012 the EU will consider the HBP and five other candidate science projects. In February 2013 a decision will be made on which of the two candidates will each receive €1 billion in funding over ten years. The chosen two projects will then run from 2013 to 2023. If the HBP is chosen, the Blue Brain Project will become a central part of it.
Jun 20, 2012  -   Two newly published video talks which share lots of detail about the Blue Brain Project simulations and visualisations. The talks were given at the INCF Multiscale Modeling Program Workshop in Stockholm on May 31 and June 1, 2012.
Jun 11, 2012  -   Scientific American has published a featured article by Henry Markram. Available online behind a $6 paywall: A countdown to a digital simulation of every last neuron in the human brain. See also the associated video animation: Neuron to cortical column.
May 24, 2012  -   New video of Henry Markram talking about the Blue Brain Project. Includes Markram's thoughts on consciousness, autism, and the Human Brain Project. Recorded in Barcelona on May 22, 2012.
Mar 30, 2012  -   The ETH Board has requested CHF 85 million (€70 m) from the Swiss government to fund the Blue Brain Project during 2013 to 2016.
Jan 3, 2012  -   FET Flagships mid-term conference presentation is now available online: Introducing the Human Brain Project

Data acquisition

There are three main steps to building the virtual brain: 1) data acquisition, 2) simulation, 3) visualisation of results.

Data acquisition involves taking brain slices, placing them under a microscope, and measuring the shape and electrical activity of individual neurons. This is how the different types of neuron are studied and catalogued. The neurons are typed by morphology (i.e. their shape), electrophysiological behaviour, location within the cortex, and their population density. These observations are translated into mathematical algorithms which describe the form, function, and positioning of neurons. The algorithms are then used to generate biologically-realistic virtual neurons ready for simulation.

One of the methods is to take 300 µm-thick sagittal brain slices from the somatosensory cortex (SA1) of juvenile Wistar rats (aged 14 to 16 days). The tissue is stained with biocytin and viewed through a bright field microscope. Neuronal 3D morphologies are then reconstructed using the Neurolucida software package (pictured below, far right) which runs on Windows workstations. Staining leads to a shrinkage of 25% in thickness and 10% in length, so the reconstruction process corrects for this. Slicing also severs 20% to 40% of axonal and dendritic arbors, so these are regrown algorithmically.

The electrophysiological behaviour of neurons is studied using a 12 patch clamp instrument (pictured below left). This tool was developed for the Blue Brain Project and it forms a foundation of the research. It enables twelve living neurons to be concurrently patched and their electrical activity recorded. The Nomarski microscope enhances the contrast of the unstained samples of living neural tissue. Carbon nanotube-coated electrodes can be used to improve recording.

Around 200 different types of ion channel are found in the cell membranes of cortical neurons. Different types of neuron have different mixes of channels - and this contributes to differences in their electrical behaviour. The genes for these channels are cloned at the lab, overexpressed in cultured cells, and their electrical behaviour recorded. Over 270 genes are known to be associated with voltage-gated ion channels in the rat. The results of this work are publicly available online at Channelpedia.



The primary software used by the BBP for neural simulations is a package called NEURON. This was developed starting in the 1990s by Michael Hines at Yale University and John Moore at Duke University. It is written in C, C++, and FORTRAN. The software continues to be under active development and, as of July 2012, is currently at version 7.2. It is free and open source software, both the code and the binaries are freely available on the website. Michael Hines and the BBP team collaborated in 2005 to port the package to the massively parallel Blue Gene supercomputer.

Simulation speed

In 2012 simulations of one cortical column (~10,000 neurons) run at approximately 300 x slower than real time. So one second of simulated time takes about five minutes to complete. The simulations show approximately linear scaling - that is, doubling the size of the neural network doubles the time it takes to simulate. Currently the primary goal is biological validity rather than performance. Once it's understood which factors are biologically important for a given effect it might be possible to trim components that don't contibute in order to improve performance.

The simulation timestep for the numerical integrations is 0.025 ms and the timestep for writing the output to disk is 0.1 ms.


The simulation step involves synthesising virtual cells using the algorithms that were found to describe real neurons. The algorthims and parameters are adjusted for the age, species, and disease stage of the animal being simulated. Every single protein is simulated, and there are about a billion of these in one cell. First a network skeleton is built from all the different kinds of synthesised neurons. Then the cells are connected together according to the rules that have been found experimentally. Finally the neurons are functionalised and the simulation brought to life. The patterns of emergent behaviour are viewed with visualisation software.

A basic unit of the cerebral cortex is the cortical column. Each column can be mapped to one function, e.g. in rats one column is devoted to each whisker. A rat cortical column has about 10,000 neurons and is about the size of a pinhead. The latest simulations, as of November 2011, contain about 100 columns, 1 million neurons, and 1 billion synapses. A real life rat has about 100,000 columns in total, and humans have around 2 million. Techniques are being developed for multiscale simulation whereby active parts of the brain are simulated in great detail while quiescent parts are not so detailed.

Every two weeks a column model is run. The simulations reproduce observations that are seen in living neurons. Emergent properties are seen that require larger and larger networks. The plan is to build a generalised simulation tool, one that makes it easy to build circuits. There are also plans to couple the brain simulations to avatars living in a virtual environment, and eventually also to robots interacting with the real world. The ultimate aim is to be able to understand and reproduce human consciousness.


The BBP-SDK (Blue Brain Project - Software Development Kit) is a set of software classes (APIs) that allows researchers to utilize and inspect models and simulations. The SDK is a C++ library wrapped in Java and Python.

Visualisation of results


RTNeuron is the primary application used by the BBP for visualisation of neural simulations. The software was developed internally by the BBP team. It is written in C++ and OpenGL. RTNeuron is ad-hoc software written specifically for neural simulations, i.e. it is not generalisable to other types of simulation. RTNeuron takes the output from Hodgkin-Huxley simulations in NEURON and renders them in 3D. This allows researchers to watch as activation potentials propogate through a neuron and between neurons. The animations can be stopped, started and zoomed, thus letting researchers interact with the model. The visualisations are multi-scale, that is they can render individual neurons or a whole cortical column. The image right was rendered in RTNeuron, as was the video seen here.

Computer hardware / Supercomputers

Blue Gene/P

The primary machine used by the Blue Brain Project is a Blue Gene supercomputer built by IBM. This is where the name "Blue Brain" originates from. IBM agreed in June 2005 to supply EPFL with a Blue Gene/L as a "technology demonstrator". The IBM press release did not disclose the terms of the deal. In June 2010 this machine was upgraded to a Blue Gene/P. The machine is installed on the EPFL campus in Lausanne (Google map) and is managed by CADMOS (Center for Advanced Modelling Science).

The computer is used by a number of different research groups, not exclusively by the Blue Brain Project. In mid-2012 the BBP was consuming about 20% of the compute time. The brain simulations generally run all day, and one day per week (usually Thursdays). The rest of the week is used to prepare simulations and to analyze the resulting data. The supercomputer usage statistics and job history are publicly available online - look for the jobs labelled "C-BPP".

Blue Gene/P technical specifications:

  • 4,096 quad-core nodes (16,384 cores in total)
  • Each core is a PowerPC 450, 850 MHz
  • Total: 56 teraflops, 16 terabytes of memory
  • 4 racks, one row, wired as a 16x16x16 3D torus
  • 1 PB of disk space, GPFS parallel file system
  • Operating system: Linux SuSE SLES 10
  • Public front end: and processing log

This machine peaked at 99th fastest supercomputer in the world in November 2009. By June 2011 it had dropped to 343th in the world. It has since dropped out of the top 500. See the Blue Gene/P ranking on the TOP500 list. More details and photos: CADMOS Blue Gene/P presentation (PDF).

Blue Gene/P architecture Blue Gene/P cabinet

Silicon Graphics

A 32-processor Silicon Graphics Inc. (SGI) system with 300 Gb of shared memory is used for visualisation of results.

Commodity PC clusters

Clusters of commodity PCs have been used for visualisation tasks with the RTNeuron software. A research paper published by the BBP team in 2012 describes the following setup:

  • 11 node cluster, 3.47 GHz processors (Intel Xeon X5690)
  • 24 GB RAM, 3 Nvidia GeForce GTX 580 GPUs
  • Full-HD passive stereo display connected to two GPUs on head node
  • 1 Gbit/s, 10 Gbit/s ethernet, 40 Gbit/s QDR InfiniBand

It's not known where this cluster is physically located - either in the BBP lab itself, in an EPFL data center, or elsewhere.


JuQUEEN is an IBM Blue Gene/Q supercomputer that was installed at the Jülich Research Center in Germany in May 2012. It currently performs at 1.6 petaflops and was ranked the world's 8th fastest supercomputer in June 2012. It's likely that this machine will be used for BBP simulations starting in 2013, provided funding is granted via the Human Brain Project.

In October 2012 the supercomputer is due to be expanded with additional racks. It is not known exactly how many racks or what the final processing speed will be.

The JuQUEEN machine is also to be used by the JuBrain (Jülich Brain Model) research initiative. This aims to develop a three-dimensional, realistic model of the human brain. This is currently separate from the Blue Brain Project but it will become part of the Human Brain Project if the latter is chosen for EU funding in late 2012.

DEEP - Dynamical Exascale Entry Platform

DEEP ( is an exascale supercomputer to be built at the Jülich Research Center in Germany. The project started in December 2011 and is funded by the European Union's 7th framework programme. The three-year protoype phase of the project has received €8.5 million. A prototype supercomputer that will perform at 100 petaflops is hoped to be built by the end of 2014.

The Blue Brain Project simulations will be ported to the DEEP prototype to help test the system's performance. If successful, a future exascale version of this machine could provide the 1 exaflops of performance required for a complete human brain simulation by the 2020s.

The DEEP prototype will be built using Intel MIC (Many Integrated Cores) processors, each of which contains over 50 cores fabricated with a 22 nm process. These processors were codenamed Knights Corner during development and subsequently rebranded as Xeon Phi in June 2012. The processors will be publicly available in late 2012 or early 2013 and will offer just over 1 teraflop of performance each.


Year TwoAugust 2011
Year One: January 2010

More videos


The project is funded primarily by EPFL, which in turn is funded by the Swiss government. EPFL is one of only two federally-funded universities in Switzerland, the other being ETH in Zurich. The BBP has additionally received funding from EU research grants, foundations, other entities, and individuals. Henry Markram mentioned in an interview in 2009 that there was "one special visionary donor" but he didn't specify exactly who.

In March 2012 the ETH Board requested CHF 85 million (€70 m) from the Swiss government to fund the Blue Brain Project during 2013 to 2016.

IBM has not funded the project, but they sold their Blue Gene supercomputer to EPFL at a reduced cost. This was because at the time the computer was a prototype and IBM was interested in testing the machine on different applications.

An application has been made for an EU FET Flagship grant for the Human Brain Project. This would provide €1 billion in funding over ten years. If the grant is awarded then the BBP will become a key part of the Human Brain Project and will share some of the funding. A decision on this award is expected in February 2013.


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.

  • Idan Segev and team at Hebrew University in Jerusalem, Israel
  • Phil Goodman of the University of Reno, Nevada
  • Michael Hines of Yale University, author of NEURON simulator, MP enhancements
  • Alex Thomson, School of Pharmacy, University of London
  • Yun Wang, St. Elizabeth's Medical Center, Boston (MA)

Project timeline

2002  Henry Markram founds the Brain Mind Institute (BMI) at EPFL
2005  June - EPFL and IBM agree to launch Blue Brain Project, IBM installs Blue Gene
Basic simulation of single neurons achieved
2006  Basic parallelization of simulation code achieved December - auto-generated cortical column simulated, shown to be biologically valid
2007  November - modeling and simulation of first rat cortical column
2008  Cortical column construction and simulations
Neocortical column (10,000 cells)
Research on determining position and size of functional cortical columns
2009  June - BlueGene/L replaced by BlueGene/P, doubling of processors
Simulations of cortical construction continue
2010  December - apply for FP7 grant
2011  Designing the FP7 project
Simulation of multiple columns, cellular mesocircuit of 100 columns
September - move into larger dedicated office space in 3rd floor of the building
2012  April - completion of the FET Flagships one-year pilot phase.
2013  February - decision expected on Human Brain Project funding of €1 billion over 10 years from the EU
Simulations using NEURON software ported to the Blue Gene/Q system in Jülich
2014  Cellular-level simulation of the entire rat brain neocortex, ~100 mesocircuits
NEURON simulation software ported to the DEEP Cluster-Booster prototype system in Jülich
2020  Exascale simulations start on the DEEP Cluster-Booster production system in Jülich
2023  Cellular-level simulation of the entire human brain, equivalent to 1,000x the size of the rat brain


Four broad motivations behind the Blue Brain Project are:
  • Brain disease treatments
  • Scientific curiosity about consciousness and the human mind
  • Integration of all neuroscientific research results worldwide
  • Progress towards building thinking machines (bottom up approach)

One in four people will suffer from one of around 560 brain diseases during their lifetime. Therefore it is important to have a good strategy for understanding these diseases and finding suitable treatments. The living brain is very difficult to study. Both from a technical perspective, and a moral one. A virtual model, however, makes direct observations possible. Experiments on models are also more efficient and limit the need for laboratory animals. The Blue Brain Project, by including molecular-level simulations, could be used to study the effect of new pharmaceutical compounds on virtual brains of any species, age, and stage of disease.

Another aim of the Blue Brain Project is to provide a centrally coordinated resource for the 200,000 active neuroscientists in the world. Previously each researcher has focused on their own specialist field without the results being shared and easily available to all. The BBP hopes to build a bigger, better platform for neuroscientists to experiment on. The project is becoming a brain simulation facility that is accessible to all.

People involved

Abdeladim Elhamdani   Linkedin profile Facebook profile Homepage
Neural and microcircuitry lab manager (LNMC) 
  Ahmet Bilgili   Google profile Linkedin profile Homepage Homepage
Computer graphics, large-scale volume rendering 
  Alejandro Schiliuk   Linkedin profile Facebook profile Homepage
Operations manager, logistics, staffing 
Alvaro Martinez   Google profile
Cajal Blue Brain, software developer 
  Bruno Magalhaes   Homepage
Software engineer, parallel multi-core HPC 
  Daniel Keller   Homepage
Molecular and subcellular neuronal simulation 
Daniel Nachbaur   Google profile
  Deborah La Mendola   Homepage
Autism research using rat brains 
  Dimitri Christodoulou   Homepage
Morphological reconstruction of neurons 
Eilif Muller   Linkedin profile Facebook profile Homepage
Development of neocortical tissue model 
  Emmanuelle Logette   Facebook profile Homepage
Electrophysiology of ~200 membrane channels 
  Farhan Tauheed   Google profile Linkedin profile Homepage
Spatial data, indexing, data mining 
Felix Schürmann   Google profile Homepage
Blue Brain General Project Manager 
  Gabriel Mateescu   Linkedin profile Homepage
Senior HPC architect, large-scale simulation 
  Georges Khazen   Linkedin profile Facebook profile Homepage
Molecular composition of neocortical neurons 
Henry Markram   Linkedin profile Wikipedia page Homepage
Project director 
  James King   Homepage
Supercomputer libraries/tools for simulations 
  Jean-Denis Courcol   Homepage
Software developer 
Jean-Pierre Ghobri   Homepage
Quantitative data on neurons in the brain 
  Jesper Ryge   Google profile Linkedin profile Homepage
Neuron morphology, behaviour, genetics 
  Joe Graham   Google profile Linkedin profile Homepage
Algorithmic generation of virtual neurons 
Juan Hernando   Homepage
Cortical circuit visualisation development 
  Julian Shillcock   Google profile Linkedin profile Homepage
Sub-cellular modeling group leader 
  Julie Meystre   Linkedin profile Homepage
Molecular biology, cell cultures, immunohistology 
Kamila Markram   Google profile Linkedin profile Homepage Homepage
Autism project manager 
  Marc-Oliver Gewaltig   Google profile Homepage
Neurorobotics group manager 
  Martin Telefont   Google profile Linkedin profile Homepage
Biomedical information collection, proteomics 
Maurizio Pezzoli   Homepage
Electrophysiology of neurons in brain slices 
  Melissa Cochrane   Google profile Linkedin profile Homepage
Scientific assistant 
  Michael Reimann   Google profile Homepage
Emergent properties of simulated microcircuits 
Monica Favre   Homepage
Study of mesolimbic circuitry in autism 
  Nenad Buncic   Linkedin profile Twitter profile Homepage
Senior project manager, technology vision 
  Rajnish Ranjan   Google profile Homepage
Ion channel database, 
Richard Walker   Homepage
Science writer, funding proposals and papers 
  Robert Bishop   Linkedin profile
Chairman advisory board 
  Rodrigo Perin   Linkedin profile Homepage
Patch-clamp recording of neurons 
Sean Hill   Google profile Linkedin profile Homepage Homepage
Project Manager for Computational Neuroscience 
  Sebastien Lasserre   Linkedin profile Homepage
Software engineering, real-time visualization 
  Shruti Muralidhar   Google profile Homepage
Characterisation of rat neocortex layer 1 
Srikanth Ramaswamy   Google profile Linkedin profile Homepage
Synapse recording and modeling 
  Stefan Eilemann   Google profile Linkedin profile Facebook profile
Visualisation architect for exascale simulations 
  Valentin Hänel   Homepage Homepage
Computational neuroscientist 
Vincent Delattre   Linkedin profile Homepage
In vitro recording of neurons using microchips 
  Werner Van Geit   Google profile Linkedin profile Homepage
Software development, automated neuron modeling 
  Yihwa Kim  
Cell morphologies, artificial generation 
Ying Shi   Linkedin profile Homepage
Software development, neural morphology algorithms 

Research papers