Henry Markram's Blue Brain Project Vies for €1 billion Prize

[Henry] Markram, a South-African-born brain electrophysiologist who joined the Swiss Federal Institute of Technology in Lausanne (EPFL) a decade ago, may soon see his ambition fulfilled. The project is one of six finalists vying to win €1 billion (US$1.3 billion) as one of the European Union's two new decade-long Flagship initiatives. _Nature

The Challenge
The Blue Brain Project in Lausanne is attempting to model a working human brain to an exquisite level of physiologic detail -- to the ion channel level. This would require an enormous amount of computer power -- to say nothing of the electrical power required to drive and cool the apparatus when fully functional. But €1 billion can presumably still buy a great deal.

Every experiment at least tacitly involves a model, whether it is the molecular structure of an ion channel or the dynamics of a cortical circuit. With computers, Markram realized, you could encode all of those models explicitly and get them to work together. That would help researchers to find the gaps and contradictions in their knowledge and identify the experiments needed to resolve them.

Markram's insight wasn't original: scientists have been devising mathematical models of neural activity since the early twentieth century, and using computers for the task since the 1950s (see page 462). But his ambition was vast. Instead of modelling each neuron as, say, a point-like node in a larger neural network, he proposed to model them in all their multi-branching detail — down to their myriad ion channels (see 'Building a brain'). And instead of modelling just the neural circuits involved in, say, the sense of smell, he wanted to model everything, “from the genetic level, the molecular level, the neurons and synapses, how microcircuits are formed, macrocircuits, mesocircuits, brain areas — until we get to understand how to link these levels, all the way up to behaviour and cognition”. _Nature...BlueBrain

Henry Markram

By the end of 2005, his team had integrated all the relevant portions of this data set into a single-neuron model. By 2008, the researchers had linked about 10,000 such models into a simulation of a tube-shaped piece of cortex known as a cortical column. Now, using a more advanced version of Blue Gene, they have simulated 100 interconnected columns.

The effort has yielded some discoveries, says Markram, such as the as-yet unpublished statistical distribution of synapses in a column. But its real achievement has been to prove that unifying models can, as promised, serve as repositories for data on cortical structure and function. Indeed, most of the team's efforts have gone into creating “the huge ecosystem of infrastructure and software” required to make Blue Brain useful to every neuroscientist, says Markram. This includes automatic tools for turning data into simulations, and informatics tools such as http://channelpedia.net — a user-editable website that automatically collates structural data on ion channels from publications in the PubMed database, and currently incorporates some 180,000 abstracts.

The ultimate goal was always to integrate data across the entire brain, says Markram. The opportunity to approach that scale finally arose in December 2009, when the European Union announced that it was prepared to pour some €1 billion into each of two high-risk, but potentially transformational, Flagship projects. Markram, who had been part of the 27-member advisory group that endorsed the initiative, lost no time in organizing his own entry. And in May 2011, the HBP was named as one of six candidates that would receive seed money and prepare a full-scale proposal, due in May 2012. _Nature

Blue Brain Model
The project is both ambitious and expensive. It is also more complex and difficult than can currently be anticipated or planned for. But kudos to Henry Markram for making the attempt.


H/T NextBigFuture

Human Brain Project Moves Toward Human Cortex Model

Spiegel
Henry Markram's Human Brain Project in Lausanne, is competing for funding from the FET Flagship Initiative, to the tune of 1 billion Euros, disbursed over a ten year period. Markram's goals are extremely ambitious, and unprecedented. He aims to model the human cerebral cortex to an exquisite degree of precision. Markram expects that his model of the human brain will be so exact, that he will be able to study inaccessible brain diseases and devise impossible brain cures by using his model. He may be right. But in only ten years?

Scientists are paying particular attention to the cerebral cortex. This layer on the outside of brain, only a few millimeters thick, is the most important condition of it evolution. It is the starting point for efforts to understand what makes us tick -- and for endeavors to find solutions when things go wrong. Our brain builds its version of the universe in the cerebral cortex. The vast majority of what we see doesn't enter the brain through the eye. It is instead is based on the impressions, experiences and decisions in our brain.

Markham already completed important preparatory work for the computer modeling of the brain with his Blue Brain Project, an attempt to understand and model the molecular makeup of the mammalian brain. He modeled a tiny part of a rat brain, a so-called neocortical column, at the cell level. To understand what one of these columns does, it's helpful to imagine the cerebral cortex as a giant piano. There are millions of neocortical columns on the surface, and each of them produces a tone, in a manner of speaking. When they are simulated, the columns produce a symphony together. Understanding the design of these neocortical columns is a holy grail of sorts for neuroscientists.

It is important to understand the rules of communication among the nerve cells. The individual cells do not communicate at random, but instead seek specifically targeted communication partners. The axes of nerve cells intersect at millions of different points, where they can form a synapse. This makes communication between individual neurons possible. In a recent article in the journal Proceedings of the National Academy of Sciences, Markram writes that such connections are also developed entirely without external influence. This could indicate a sort of innate knowledge that all people have in common. Markram refers to it as the "Lego blocks" of the brain, noting that each person assembles his own world on the basis of this innate knowledge. _Spiegel

The object of study for the Human Brain Project may be the most complex dynamic system in the universe. The attempt would be impossible without the most sophisticated computing hardware and software available. And one must have more than a mere fistful of Euros to acquire such advanced goodies.

Modeling all of this in a computer is extremely complex. Markram's current model encompasses tens of thousands of neurons. But this isn't nearly enough to come within striking range of the secret of our brain. To do that, scientists will have to assemble countless other partial models, which are to be combined to create a functioning total simulation by 2023.

The supercomputers at the Jülich Research Center near Cologne are expected to play an important role in this process. The brain simulation will require an enormous volume of data, or what scientist Markram calls a "tsunami of data." One of the challenges for scientists working under Thomas Lippert, head of the Jülich Supercomputing Centre, is to figure out how to make the computer process only a certain part of the data at a given time, but without completely losing sight of the rest. They also have to develop an imaging method, such as large, three-dimensional holograms, to depict the massive amounts of data.

All it takes is a look at the work of Jülich neuroscientist Katrin Amunts to understand the sheer volume of information at hand. The team she heads is compiling a detailed atlas of the human brain. To do so, they cut a brain into 8,000 slices and digitized them with a high-performance scanner. The brain model generated in this way consists of cuboids, each measuring 10 by 10 by 20 micrometers, and the size of the data set is three terabytes. Brain atlases with higher resolutions, says Amunts, would probably consist of more than 700 terabytes _Spiegel

The answer to the question posed above is: No, this goal cannot be met within a time frame of ten years. Because the challenge is not merely quantitative -- a matter of compiling the precise assembly of terabytes to create a brain atlas. The goal is to create a dynamic, interactive model of incredible plasticity -- a model which changes itself moment to moment. The "700 terabyte" requirement mentioned above is just the starting point -- the bare beginning -- in the assembly of such a dynamic and ever-changing model.

But the problem is even harder -- much, much harder. The quantitative complexity -- even in dynamic flow -- is nothing when compared to the qualitative complexity, which is nowhere near to being solved by Markram's team.

The project as described in brief above is an excellent starting point. Much can be learned from such an approach. But starting points do not necessarily point directly toward the end that one seeks. Rather, they point somewhere "out there." It is for the questers to continuously adjust their headings -- and often they are forced to adjust their goals.

Good luck to Henry and his team -- with the funding and with the ongoing project. It is an ambitious goal worthy of any scientist.

Blue Brain Tour in Lausanne: What Is Happening?

Bluebrain | Year One from Couple 3 Films on Vimeo.

Henry Markram brings us up to date on the aims of Blue Brain.  H/T MachinesLikeUs

The Swiss Blue Brain Project is meant to provide a "machine model" of the brain, for purposes of neuroscientific study.  It does not mean to build a machine intelligence.  Instead, Blue Brain is a massive simulation of brain function down to the level of ion channels within individual neurons.

Markram expects the project to provide a means for solving the diseases of the brain -- including Alzheimer's, Parkinson's, Huntington's, and others -- at a cellular and molecular level.

Clearly, Blue Brain is a massively ambitious and expensive project.  How soon will it begin to pay for itself by solving the critical problems of brain degeneration and other deficits of brain function?  It is impossible to say.

The video segment above is the "year one" report out of a "ten year project" to document the Blue Brain project on film.

More On IBM's BlueMatter "Brain Simulation"

Popular Mechanics provides information on a recent IBM "brain simulation" of 1.6 billion simulated neurons -- an attempt to "simulate the human visual cortex". Here are some fascinating details from PM:

Modha's billion-neuron virtual cortex is so massive that running it required one of the fastest supercomputers in the world—Dawn, a Blue Gene/P supercomputer at Lawrence Livermore National Laboratory (LLNL) in California.


Dawn hums and breathes inside an acre-size room on the second floor of the lab's Terascale Simulation Facility. Its 147,456 processors and 147,000 gigabytes of memory fill 10 rows of computer racks, woven together by miles of cable. Dawn devours a million watts of electricity through power cords as thick as a bouncer's wrists—racking up an annual power bill of $1 million. The roar of refrigeration fans fills the air: 6675 tons of air-conditioning hardware labor to dissipate Dawn's body heat, blowing 2.7 million cubic feet of chilled air through the room every minute.


Dawn was installed earlier this year by the Department of Energy's National Nuclear Security Administration (NNSA), which conducts massive computer simulations to ensure the readiness of the nation's nuclear weapons arsenal. Modha's team worked with Dawn for a week before it was transitioned to NNSA's classified nuclear work. For all of its legendary computing power, Dawn still ran Modha's 1.6 billion neurons at only one-six-hundredth the speed of a living brain. A second simulation, with 1 billion neurons, ran a little faster—but still only at one-eighty-third of normal brain speed.


These massive simulations are merely steps toward Modha's ultimate goal: simulating the entire human cortex, about 25 billion neurons, at full speed. To do that, he'll need to find 1000 times more computing power. At the rate that supercomputers have expanded over the last 20 years, that super-super computer could exist by 2019. "This is not just possible, it's inevitable," Modha says. "This will happen." _PM

Can you imagine $1 million a year just to power the processors? 6675 "tons" of air conditioning to keep the hardware cool? All to run a very poor simulation of a human visual cortex at 1 / 600 th the speed of a human brain?

You have to admire Modha's optimism when he claims that it is inevitable that the human level artificial cortex will be operating in real time by the year 2019. As I said a couple of days ago, the estimation is preposterous.

First of all, the current simulation is of the most basic cortical neural architecture. It is the easiest, least complex type of neural simulation problem to solve. In fact, an insect brain is more complex -- with far fewer neurons, at much faster speeds, in a far smaller space, with a far lower energy budget -- than the monstrosity that IBM has strung together at Livermore.

It looks as if Modha's team is in a race with Henry Markram's Swiss team, and other teams around the world to simulate the human cortex the soonest. Perhaps there is an X prize for that achievement. But it seems to Al Fin engineers that this brute force approach is best adapted for burning up research funds, rather than actually coming close to simulating a human brain.

Yes, you must crawl before you can walk, walk before you can run.  But it seems as if what the IBM team is doing is moving two fingers in the air in a crawling motion, and calling it crawling.  Time will tell.

As I said before, the achievement of simulating some basic quasi-physiologic responses to simulated input stimuli -- on this scale -- is remarkable.  This platform will provide for some fascinating lab experiments in artificial neuronal networks.  There is a lot to be learned.

But please go easy on the claims.  The field of artificial intelligence has left a junkyard of exaggerated claims and absurd unfulfilled predictions across the landscape of the past 50 years.  Stick to the facts.

Actually, No, IBM Did NOT Simulate a Cat's Brain

Cognitive computing seeks to engineer the mind by reverse engineering the brain. The mind arises from the brain, which is made up of billions of neurons that are liked by an internet like network. An emerging discipline, cognitive computing is about building the mind, by understanding the brain. It synthesizes neuroscience, computer science, psychology, philosophy, and mathematics to understand and mechanize the mental processes. Cognitive computing will lead to a universal computing platform that can handle a wide variety of spatio-temporally varying sensor streams. _Source

Image via Brian WangAt a supercomputing conference in Portland, Oregon, IBM announced a successful simulation of a "cat brain scale" silicon neuro-computing platform. You can read an engrossing account of this achievement from Dharmendra S. Modha (via Brian Wang).

The achievement is a fascinating one, and worth celebrating. But it would be inaccurate to describe the accomplishment as the simulation of a cat brain. IBM's simulation is nowhere close to a simulation of a cat's brain -- and we should have no illusions regarding the comparison. Consider this:

...using Dawn Blue Gene / P supercomputer at Lawrence Livermore National Lab with 147,456 processors and 144 TB of main memory, we achieved a simulation with 1 billion spiking neurons and 10 trillion individual learning synapses. This is equivalent to 1,000 cognitive computing chips each with 1 million neurons and 10 billion synapses, and exceeds the scale of cat cerebral cortex. The simulation ran 100 to 1,000 times slower than real-time. _ Modha

Some of the neuronal level phenomena observed in the simulation (in response to stimuli) vaguely resembled actual neurocortical electrophysiological activity observed in a mammalian brain. But the parallels are quite loose. And considering the time scale of the simulation was between 100 and 1,000 times slower than real-time, tweaking the stimuli and response to better match mammalian equivalents may take some while.

IBM's simulation of 1 billion spiking neurons was interesting, but a very pale shadow of a genuine cat brain, with all its specialised structural cortical and sub-cortical components -- and accessory (but very influential) glial and vascular infrastructure.

IBM is estimating that around the year 2018, it will be able to simulate a human-scale "brain" in real time. That is preposterous. Only if you accept that IBM has simulated a cat's brain in 2009 would you accept that IBM may simulate a human brain by 2018. But as IBM readily admits, it has not even simulated a cat's multi-specialised cortex -- much less a cat's entire brain.

In terms of details in our simulations, we are currently working on differentiating our cortical region into specific areas (such as primary visual cortex or motor cortex) and providing the long-range connections that form the circuitry between these areas in the mammalian brain. For this work, we are drawing from many studies describing the structure and input/output patterns of these areas as well as a study recently performed within IBM that collates a very large number of individual measurements of white matter, the substrate of long-range connectivity within the brain. _Modha

Give them time. I expect some amazing results from this approach to brain simulation, eventually. Unfortunately, neither the hardware nor the software tools are anywhere close to providing scientists with a semi-realistic mammalian brain at this time.

Even a realistic insect brain would be a marvelous achievement. Particularly one that operated in real time, and was sized on roughly the same scale as an actual insect.

So while Al Fin cognitive neuroscientists, engineers, and cognitive computing specialists congratulate IBM on its latest achievement, they also caution IBM not to overstate what they have actually accomplished.

25 Nov 2009 Update: Be sure to read Henry Markram's response to the IBM claims at Brian Wang's site (and in comments here).

Rat Cortical Column Simulation Update: From Here to an Artificial Brain?

One of the holy grails of neuroscience is the creation of an accurate simulation of mammalian brain cortex. Swiss researchers have been working on the "Blue Brain" project since 2005, and are collaborating with IBM researchers to simulate the neocortex.

By mimicking the behavior of the brain down to the individual neuron, the researchers aim to create a modeling tool that can be used by neuroscientists to run experiments, test hypotheses, and analyze the effects of drugs more efficiently than they could using real brain tissue.

The model of part of the brain was completed last year, says Markram. But now, after extensive testing comparing its behavior with results from biological experiments, he is satisfied that the simulation is accurate enough that the researchers can proceed with the rest of the brain.

"It's amazing work," says Thomas Serre, a computational-neuroscience researcher at MIT. "This is likely to have a tremendous impact on neuroscience."

TechReview

The neocortical column is considered the functional building block of the mammalian cortex--a logical unit of brain organisation to begin a useful brain simulation project.

The project began with the initial goal of modeling the 10,000 neurons and 30 million synaptic connections that make up a rat's neocortical column, the main building block of a mammal's cortex. The neocortical column was chosen as a starting point because it is widely recognized as being particularly complex, with a heterogeneous structure consisting of many different types of synapse and ion channels. "There's no point in dreaming about modeling the brain if you can't model a small part of it," says Markram.

The model itself is based on 15 years' worth of experimental data on neuronal morphology, gene expression, ion channels, synaptic connectivity, and electrophysiological recordings of the neocortical columns of rats. Software tools were then developed to process this information and automatically reconstruct physiologically accurate 3-D models of neurons and their interconnections.

The researchers now think they have their neocortical column model well enough perfected to begin working on an entire mammalian "brain." They think they can model a mammalian brain realistically within 3 years, but respected neuro-researcher Christof Koch says "not so fast!"

However, none of these results have so far been published in the peer-reviewed literature, says Christof Koch, a professor of biology and engineering at Caltech. And this is by no means the first computer model of the brain, he points out. "This is an evolutionary process rather than a revolutionary one," he says. As long ago as 1989, Koch created a 10,000-neuron simulation, albeit in a far simpler model.

Furthermore, Koch is skeptical about how quickly the brain model can progress. Any claims that the human brain can be modeled within 10 years are so "ridiculous" that they are not worth discussing, he says.

Rat brains have about 200 million neurons, while human brains have in the region of 50 to 100 billion neurons. "That is a big scale-up," admits Markram.

source

The simulation is at a cellular level, and the researchers want to go deeper to the molecular level. This will put a tremendous strain on the computational infrastructure of the system. And it is not clear what is to be gained at this early stage by going to molecular resolution. Particularly when the cortical function appears to be at least partially based upon oscillatory phase-locking of assembles of neurons, such as columns and columnar groups.

Perhaps the Swiss researchers' "bottom-up" approach, combined with "top-down" approaches by people such as Jeff Hawkins, will begin to simulate some of the function of the human neocortex within the next 15 years. Perhaps.