Brain simulation raises questions

What does it mean to simulate the human brain? Why is it important to do so? And is it even possible to simulate the brain separately from the body it exists in? These questions are discussed in a new paper published in the scientific journal Neuron today.

Simulating the brain means modeling it on a computer. But in real life, brains don’t exist in isolation. The brain is a complex and adaptive system that is seated within our bodies and entangled with all the other adaptive systems inside us that together make up a whole person. And the fact that the brain is a brain inside our bodies is something we can’t ignore when we attempt to simulate it realistically. Today, two Human Brain Project (HBP) researchers, Kathinka Evers, philosopher at the Centre for Research Ethics and Bioethics at Uppsala University and Yadin Dudal, neuroscientist at the Weizmann Institute of Science, publish a paper in Neuron that discusses the questions raised by brain simulations within and beyond the EU flagship project HBP.

For many scientists, understanding means being able to create a mental model that allows them to predict how a system would behave under different conditions. For the brain sciences, this type of understanding is currently only possible for a limited number of basic functions. In the article, Kathinka Evers and Yadin Dudal discuss the goal of simulation. In broad terms it has to do with understanding. But what does understanding mean in neuroscience?

As it dwells inside our bodies, the brain is always a result of what the individual has experienced up to that point. That is why, when we simulate the brain, we have to take this ‘experienced brain’ into account and try and reflect that.

According to Kathinka Evers, leader of the Ethics and Society part of the Human Brain Project, neglecting this experience would severely limit the outcome of any brain simulation. But if we are to include experience we have to simulate real-life situations.

“That is a daunting task: a large part of that experience is the brain’s interaction with the rest of the human body existing and interacting in a still larger social context”, says Kathinka Evers.

What outcome would be realistic to hope for in the Human Brain Project’s simulation? In neuroscience, computer simulations of specific systems are already in use. These simulations are a complement to other tools scientists use.

But there are some warnings to issue here. According to Kathinka Evers and Yadin Dudal, our knowledge to date is still very limited. There are many neuroscientists who think that it is too early for large scale brain simulations. Collecting the data we need for this is not an easy task. Another problem is whether we truly can understand what we are about to build. There are also technical limitations: there simply isn’t enough computing power available today.

But if we do manage to simulate the brain, would that mean we have created artificial consciousness? And can a computer be conscious at all? According to Kathinka Evers and Yadin Dudal, that depends on what consciousness is: If it is the result of certain types of organization or functions of biological matter, like the cells in the human body, then a computer can never gain consciousness. But if it is a matter of organization alone, without the need for biological matter, then the answer could be yes. But it is still a very hypothetical stance.

Thinking it through: Scientists seek to unlock mysteries of the brain

Understanding the human brain is one of the greatest challenges facing 21st century science. If we can rise to this challenge, we will gain profound insights into what makes us human, develop new treatments for brain diseases, and build revolutionary new computing technologies that will have far reaching effects, not only in neuroscience.

Scientists at the European Human Brain Project—set to announce more than a dozen new research partnerships worth Eur 8.3 million in funding later this month—the Allen Institute for Brain Science, and the US BRAIN Initiative are developing new paradigms for understanding how the human brain works in health and disease. Today, their international and collaborative projects are defined, explored, and compared during “Inventing New Ways to Understand the Human Brain,” at the 2014 AAAS Annual Meeting in Chicago.

Brain Simulation, Big Data, and a New Computing Paradigm

Henry Markram from the Ecole Polytechnique Fédérale de Lausanne (EPFL), in Switzerland, where the Human Brain Project is based, describes how the project will leverage available experimental data and basic principles of brain organization to reconstruct the detailed structure of the brain in computer models. The models will allow the HBP to run super-computer based simulations of the inner working of the brain.

"Brain simulation allows measurements and manipulations impossible in the lab, opening the road to a new kind of in silico experimentation," Markram says.

The data deluge in neuroscience is resulting in a revolutionary amount of brain data with new initiatives planning to acquire even more. But searching, accessing, and analyzing this data remains a key challenge.

Sean Hill, also of EPFL and a speaker at AAAS, leads The Neuroinformatics Platform of the Human Brain Project (HBP). In this scientific panel, he explains how the platform will provide tools to manage, navigate, and annotate spatially referenced brain atlases, which will form the basis for the HBP’s modeling effort—turning Big Data into deep knowledge.

The Neuroinformatics Platform will bring together many different kinds of data. University of Edinburgh’s Seth Grant, a key member of the HBP, describes how he is deriving new methods to decode the molecular principles underlying the brain’s organization, such as how individual proteins assemble into larger complexes. As Grant explains in Chicago, this has important practical applications as many mutations in schizophrenia and autism converge on these so-called supercomplexes in the brain.

As we understand more and more about the way the brain computes we can apply this knowledge to technology. Karlheinz Meier, of Heidelberg University in Germany and a speaker at AAAS, outlines how he is working to create entirely new computing systems as part of the HBP. These Neuromorphic Computing Systems will merge realistic brain models with new hardware for a completely new paradigm of computing—one that more closely resembles how the brain itself processes information.

"The brain has the ability to efficiently perform computations that are impossible even for the most powerful computers while consuming only 30 Watts of power," Meier says.

Brain: Get Ready For Your Close-up

At AAAS, Christof Koch lays out another ambitious, 10-year plan from the Allen Institute for Brain Science: to understand the structure and function of the brain by mapping cell types from mice and humans with computer simulations and figuring out how the cells connect, and how they encode, relay, and process information. The project, Koch says, promises massive, multimodal, and open-access datasets and methodology that will be reproducible and scalable.

At Harvard University, George Church is participating in the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, which aims to map every neuron in the brain with rapidly advancing technologies. At AAAS, he describes progress on new tools for measurements of brain cell development, connectivity, and functional state dynamics in rodent and human clinical samples.

What do all of these projects have in common? They seek to help find some of the most elusive answers known to man: what makes us human, how does the brain function, what causes neurological and mental illness, and, most importantly, how can we treat or cure these afflictions?

Energy Efficient Brain Simulator Outperforms Supercomputers

In November 2012, IBM announced that it had used the Blue Gene/Q Sequoia supercomputer to achieve an unprecedented simulation of more than 530 billion neurons. The Blue Gene/Q Sequoia accomplished this feat thanks to its blazing fast speed; it clocks in at over 16 quadrillion calculations per second. In fact, it currently ranks as the second-fastest supercomputer in the world.

But, according to Kwabena Boahen, Ph.D., the Blue Gene still doesn’t compare to the computational power of the brain itself.

"The brain is actually able to do more calculations per second than even the fastest supercomputer," says Boahen, a professor at Stanford University, director of the Brains in Silicon research laboratory and an NSF Faculty Early Career grant recipient.

That’s not to say the brain is faster than a supercomputer. In fact, it’s actually much slower. The brain can do more calculations per second because it’s “massively parallel,” meaning networks of neurons are working simultaneously to solve a great number of problems at once. Traditional computing platforms, no matter how fast, operate sequentially, meaning each step must be complete before the next step is begun.

Boahen works at the forefront of a field called neuromorphic engineering, which seeks to replicate the brain’s extraordinary computational abilities using innovative hardware and software applications. His laboratory’s most recent accomplishment is a new computing platform called Neurogrid, which simulates the activity of 1 million neurons.

Neurogrid is not a supercomputer. It can’t be used to simulate the big bang, or forecast hurricanes, or predict epidemics. But what it can do sets it apart from any computational platform on earth.

Neurogrid is the first simulation platform that can model a million neurons in real time. As such, it represents a powerful tool for investigating the human brain. In addition to providing insight into the normal workings of the brain, it has the potential to shed light on complex brain diseases like autism and schizophrenia, which have so far been difficult to model.

The proven ability to simulate brain function in real time has, so far, been underwhelming. For example, the Blue Gene/Q Sequoia supercomputer’s simulation took over 1,500 times longer than it would take the brain to do the same activity.

Cheaper brain simulation platforms that combine the computing power of traditional central processing units (CPUs) with graphical processing units (GPUs) and field programmable gate arrays (FPGAs) to achieve results comparable to the Blue Gene are emerging on the market. However, while these systems are more affordable, they are still frustratingly slower than the brain.

As Boahen puts it, “The good news is now you too can have your own supercomputer. The bad news is now you too can wait an hour to simulate a second of brain activity.”

When you consider that the simulations sometimes need to be checked, tweaked, re-checked and run again hundreds of times, the value of a system that can replicate brain activity in real time becomes obvious.

"Neurogrid doesn’t take an hour to simulate a second of brain activity," says Boahen. "It takes a second to simulate a second of brain activity."

Each of Neurogrid’s 16 chips contains more than 65,000 silicon “neurons” whose activity can be programmed according to nearly 80 parameters, allowing the researchers to replicate the unique characteristics of different types of neurons. Soft-wired “synapses” crisscross the board, shuttling signals between every simulated neuron and the thousands of neurons it is networked with, effectively replicating the electrical chatter that constitutes communication in the brain.

But the fundamental difference between the way traditional computing systems model the brain and the way Neurogrid works lies in the way the computations are performed and communicated throughout the system.

Most computers, including supercomputers, rely on digital signaling, meaning the computer carries out instructions by essentially answering “true” or “false” to a series of questions. This is similar to how neurons communicate: they either fire an action potential, or they don’t.

The difference is that the computations that underlie whether or not a neuron fires are driven by continuous, non-linear processes, more akin to an analog signal. Neurogrid uses an analog signal for computations, and a digital signal for communication. In doing so, it follows the same hybrid analog-digital approach as the brain.

In addition to its superior simulations, it also uses a fraction of the energy of a supercomputer. For example, the Blue Gene/Q Sequoia consumes nearly 8 megawatts of electricity, enough to power over 160,000 homes. Eight megawatts at $0.10/kWh is $800 an hour, or a little over $7 million a year.

Neurogrid, on the other hand, operates on a paltry 5 watts, the amount of power used by a single cell phone charger.

Ultimately, Neurogrid represents a cost-effective, energy-efficient computing platform that Boahen hopes will revolutionize our understanding of the brain.

For more information about this project, check out Dr. Boahen’s website.

Will we ever… simulate the human brain?

A billion dollar project claims it will recreate the most complex organ in the human body in just 10 years. But detractors say it is impossible. Who is right?

For years, Henry Markram has claimed that he can simulate the human brain in a computer within a decade. On 23 January 2013, the European Commission told him to prove it. His ambitious Human Brain Project (HBP) won one of two ceiling-shattering grants from the EC to the tune of a billion euros, ending a two-year contest against several other grandiose projects. Can he now deliver? Is it even possible to build a computer simulation of the most powerful computer in the world – the 1.4-kg (3 lb) cluster of 86 billion neurons that sits inside our skulls?

The very idea has many neuroscientists in an uproar, and the HBP’s substantial budget, awarded at a tumultuous time for research funding, is not helping. The common refrain is that the brain is just too complicated to simulate, and our understanding of it is at too primordial a stage.

Then, there’s Markram’s strategy. Neuroscientists have built computer simulations of neurons since the 1950s, but the vast majority treat these cells as single abstract points. Markram says he wants to build the cells as they are – gloriously detailed branching networks, full of active genes and electrical activity. He wants to simulate them down to their ion channels – the molecular gates that allow neurons to build up a voltage by shuttling charged particles in and out of their membrane borders. He wants to represent the genes that switch on and off inside them. He wants to simulate the 3,000 or so synapses that allow neurons to communicate with their neighbours.

Erin McKiernan, who builds computer models of single neurons, is a fan of this bottom-up approach. “Really understanding what’s happening at a fundamental level and building up – I generally agree with that,” she says. “But I tend to disagree with the time frame. [Markram] said that in 10 years, we could have a fully simulated brain, but I don’t think that’ll happen.”

Even building McKiernan’s single-neuron models is a fiendishly complicated task. “For many neurons, we don’t understand well the complement of ion channels within them, how they work together to produce electrical activity, how they change over development or injury,” she says. “At the next level, we have even less knowledge about how these cells connect, or how they’re constantly reaching out, retracting or changing their strength.” It’s ignorance all the way down.

“For sure, what we have is a tiny, tiny fraction of what we need,” says Markram. Worse still, experimentally mapping out every molecule, cell and connection is completely unfeasible in terms of cost, technical requirements and motivation. But he argues that building a unified model is the only way to unite our knowledge, and to start filling in the gaps in a focused way. By putting it all together, we can use what we know to predict what we don’t, and to refine everything on the fly as new insights come in.

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Why we’re building a €1 billion model of a human brain

We want to reach a unified understanding of the brain and the simulation on a supercomputer is the tool. Today you have neuroscientists working on a genetic, behavioural or cognitive level, and then you have informaticians, chemists and mathematicians. They all have their own understanding of how the brain functions and is structured. How do you get them all around the same table? We think of the project as like a CERN for the brain. The model is our way of bringing everyone, and our understanding, together.

A More Human Artificial Brain

Staying on task

Its full name is the Semantic Pointer Architecture Unified Network, but Spaun sounds way more epic. It’s the latest version of a techno brain, the creation of a Canadian research team at the University of Waterloo.

So what makes Spaun different from a mindboggingly smart artificial brain like IBM’s Watson? Put simply, Watson is designed to work like a supremely powerful search engine, digging through an enormous amount of data at breakneck speed and using complex algorithms to derive an answer. It doesn’t really care about how the process works; it’s mainly about mastering information retrieval.

But Spaun tries to actually mimic the human brain’s behavior and does so by performing a series of tasks, all different from each other. It’s a computer model that can not only recognize numbers with its virtual eye and remember them, but also can manipulate a robotic arm to write them down.

Spaun’s “brain” is divided into two parts, loosely based on our cerebral cortex and basil ganglia and its simulated 2.5 million neurons–our brains have 100 billion–are designed to mimic how researchers think those two parts of the brain interact.

Say, for instance, that its “eye” sees a series of numbers. The artificial neurons take that visual data and route it into the cortex where Spaun uses it to perform a number of different tasks, such as counting, copying the figures, or solving number puzzles.

Soon it will be forgetting birthdays

But there’s been an interesting twist to Spaun’s behavior. As Francie Diep wrote in Tech News Daily, it became more human than its creators expected.

Ask it a question and it doesn’t answer immediately. No, it pauses slightly, about as long as a human might. And if you give Spaun a long list of numbers to remember, it has an easier time recalling the ones it received first and last, but struggles a bit to remember the ones in the middle.

“There are some fairly subtle details of human behavior that the model does capture,” says Chris Eliasmith, Spaun’s chief inventor. “It’s definitely not on the same scale. But it gives a flavor of a lot of different things brains can do.”

Brain drains

The fact that Spaun can move from one task to another brings us one step closer to being able to understand how our brains are able to shift so effortlessly from reading a note to memorizing a phone number to telling our hand to open a door.

And that could help scientists equip robots with the ability to be more flexible thinkers, to adjust on the fly. Also, because Spaun operates more like a human brain, researchers could use it to run health experiments that they couldn’t do on humans.

Recently, for instance, Eliasmith ran a test in which he killed off the neurons in a brain model at the same rate that neurons die in people as they age. He wanted to see how the loss of neurons affected the model’s performance on an intelligence test.

One thing Eliasmith hasn’t been able to do is to get Spaun to recognize if it’s doing a good or a bad job. He’s working on it.

Simulated brain mimics human quirks

A new computer simulation of the brain can count, remember and gamble. And the system, called Spaun, performs these tasks in a way that’s eerily similar to how people do.

Short for Semantic Pointer Architecture Unified Network, Spaun is a crude approximation of the human brain. But scientists hope that the program and efforts like it could be a proving ground to test ideas about the brain.  

Several groups of scientists have been racing to construct a realistic model of the human brain, or at least parts of it. What distinguishes Spaun from other attempts is that the model actually does something, says computational neuroscientist Christian Machens of the Champalimaud Centre for the Unknown in Lisbon, Portugal. At the end of an intense computational session, Spaun spits out instructions for a behavior, such as how to reproduce a number it’s been shown. “And of course, that’s why the brain is interesting,” Machens says. “That’s what makes it different from a plant.”

Like a digital Frankenstein’s monster, Spaun was cobbled together from bits and pieces of knowledge gleaned from years of basic brain research. The behavior of 2.5 million nerve cells in parts of the brain important for vision, memory, reasoning and other tasks forms the basis of the new system, says Chris Eliasmith of the University of Waterloo in Canada, coauthor of the study, which appears in the Nov. 30 Science.

Input takes the form of written or typed characters, which Spaun “sees” with its vision system. The incoming information flows through the system, bouncing to and from various brain areas as it gets compressed into clear directions. Then, Spaun makes a decision about what to do. Finally, the decision gets expanded into action — it generates precise instructions on how to write out an answer. Because of the size and complexity of the system, the process is slow — in Spaun’s world, one second of work takes two real hours of computations.

IBM Research And LLNL Claim 10¹⁴ Synapse Simulation

Inspired by the function, power, and volume of the organic brain, IBMis reportedly developing TrueNorth, a novel modular, scalable, non-von Neumann, ultra-low power, cognitive computing architecture. The TrueNorth system consists of a scalable network of neurosynaptic cores, with each core containing neurons, dendrites, synapses, and axons. Also, to help the computation of TrueNorth, IBM has developed Compass, a multi-threaded, massively parallel functional simulator and a parallel compiler that maps a network of long-distance pathways in the macaque monkey brain to TrueNorth.

The research was recently presented at the Super Computing 2012 (SC12) conference in Salt Lake City.  The paper, “Compass: A scalable simulator for an architecture for Cognitive Computing" is available online.

IBM and Lawrence Livermore National Laboratory (LBNL) demonstrated near-perfect weak scaling on a 16 rack IBM Blue Gene/Q (262,144 processor cores, 256 TB memory), achieving an unprecedented scale of 256 million neurosynaptic cores containing 65 billion neurons and 16 trillion synapses running only 388× slower than real time with an average spiking rate of 8.1 Hz. By using emerging PGAS communication primitives, IBM also demonstrated 2× better real-time performance over MPI primitives on a 4 rack Blue Gene/P (16384 processor cores, 16 TB memory).

Also, since submitting the original paper, the work has continued using 96 Blue Gene/Q racks of the Lawrence Livermore National Lab Sequoia supercomputer (1,572,864 processor cores, 1.5 PB memory, 98,304 MPI processes, and 6,291,456 threads), IBM and LBNL achieved an unprecedented scale of 2.084 billion neurosynaptic cores containing 53x1010 neurons and 1.37x1014 synapses running only 1542× slower than real time. Here is PDF of IBM Research Report, RJ 10502.

As in the image above, A Network of Neurosynaptic Cores Derived from Long-distance Wiring in the Monkey Brain -Neuro-synaptic cores are locally clustered into brain-inspired regions, and each core is represented as an individual point along the ring. Arcs are drawn from a source core to a destination core with an edge color defined by the color assigned to the source core.

Scientists to simulate human brain inside a supercomputer

Scientists at its forerunner, the Switzerland-based Blue Brain Project, have been working since 2005 to feed a computer with vast quantities of data and algorithms produced from studying tiny slivers of rodent gray matter.

Last month they announced a significant advancement when they were able to use their simulator to accurately predict the location of synapses in the neocortex, effectively mapping out the complex electrical brain circuitry through which thoughts travel.

Henry Markram, the South African-born neuroscientist who heads the project, said the breakthrough would have taken “decades, if not centuries” to chart using a real neocortex. He said it was proof their concept, dubbed “brain in a box” by Nature magazine, would work.

Now the team are joining forces with other scientists to create the Human Brain Project. As its name suggests, they aim to scale up their model to recreate an entire human brain.

It is a step that will need both a huge increase in funding and access to computers so advanced that they have yet to be built.

If their current bid for €1 billion ($1.3 billion) of European Commission funding over the next 10 years is successful, Markram predicts that his computer neuroscientists are a decade away from producing a synthetic mind that could, in theory, talk and interact in the same way humans do.