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Challenges in Simulating a Human Brain

The views expressed are those of the author and are not necessarily those of Scientific American.


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Fig.1: Can the complexity of a human brain  be captured by computers? (Source: A Health Blog via Flickr)

Fig.1: Can the complexity of a human brain be captured by computers? (Source: Saad Faruque on Flickr)

The human brain is beautifully complex. And frustrating. Our understanding of it is fragmented, and hindered because, except rarely, we can’t get inside and look around while it is still functioning. Computers offer promise; if we could build an artificial brain that behaves likes a real one, maybe we could pick it apart to see how it works. This is the concept behind the Human Brain Project, recently awarded 1 billion euros by the European Commission as one of two Future and Emerging Technologies Flagships Initiatives. HBP’s co-director, Dr. Henry Markram, says they can realistically simulate a human brain within 10 years. But many neuroscientists argue we don’t know enough about the brain to model it. Where are the gaps in our knowledge and why might they present problems?

Constructing the building blocks

An artificial brain must start with good building blocks. Real neurons are diverse. They extend short and long processes, which branch out in different patterns. These shapes are not just for show; neurons process signals differently depending on their structure. One of HBP’s goals is to model neurons as 3-dimensionally detailed cells.

Neurons can be visualized under microscopes and reconstructed using computer programs. But the structure we can recreate in models is an approximation of the real one. It is difficult to mathematically represent all branches, or their turns and tapers. A greater challenge is ascribing functional properties. In many neurons, it is not clear exactly where in the structure signals from other cells are received, or how signals arriving in different branches are combined.

Neurons are also not static. Their extensions reach out or retract due to development, learning, and injury. Knowledge of how this happens is incomplete. How will modelers decide what rules to implement?

Equipping the building blocks

Fig.2: Neurons have diverse structures. (Source: Ramon y Cajal, ca. 1905)

Fig.2: Neurons have diverse structures. (Source: Ramon y Cajal, ca. 1905)

The building blocks of an artificial brain must also respond and send signals. Real neurons are populated with many different proteins. For example, channel proteins allow charged molecules, ions, to cross the membrane and generate electrical activity. While many types of ion channel have been characterized, many remain to be studied. In most neurons, the complete population of proteins present, or how they all contribute to signaling, is not known. The number and type of proteins in a neuron also changes under a variety of conditions and the mechanisms are poorly understood. Which proteins should be put into different model neurons? And when should protein expression be turned on, off, up, or down?

Finally, where should proteins be placed? Some channels are present only in cell bodies, while others are found in the extensions. The distribution of channels affects the way neurons receive and sends signals, but often isn’t known. How will morphology be coupled with function?

Building small networks

If challenges in modeling single neurons are overcome, the next step is to connect them. Communication between neurons occurs at specialized contacts. We know a lot about the composition of these contacts and the general rules of signal transmission. But important details are missing. Who is connected to whom? Where in single neurons are contacts located? What are the strengths of the connections, and how do strengths change under different conditions? For most neurons, we have limited information, such as potential partners and estimates of connection strengths. Testing all possible pairs of neurons, even within a small region of brain, is not feasible. How will we connect a network of neurons and be sure that the partners, locations, and strengths are correct? Even if the model is built such that connections and strengths can evolve, what will be the rules of evolution? These details will have profound effects on the model’s output.

Connecting across multiple levels

Fig.3: How should a model brain be connected? (Source: Li et al. 2009, PLoS Comp. Biol. 5(5):e1000395)

Fig.3: How should a model brain be connected? (Source: Li et al. 2009, PLoS Comp. Biol. 5(5):e1000395)

Many networks must be connected to form a complete brain. Although we know in general terms which brain regions talk to others, we are ignorant as to many of the details of this communication. Which neurons in which regions are connected? What are the feedback loops by which signals travels from one region to another and back again? It is also not clear how information across multiple levels of organization (molecular, cellular) and processed over multiple time scales (seconds, minutes) is integrated. How should the model be bound together?

What will a model brain do?

If all these challenges are surmounted and a human brain simulated, what might the model do? Will it reproduce behaviors that so impress us about real brains? It’s possible. Beyond a certain level of complexity, a model can do many impressive things. But the focus should be on whether we will understand how and why behaviors emerge. We want mechanisms.

The advantage of a model is that the pieces comprising it are known, and if it is sufficiently simple, pieces can be removed to examine their role. But with the level of complexity required for the proposed model, removing one component at a time would not only be extremely cumbersome, it is questionable whether it would increase our understanding. Complex behaviors, if they arise, will likely result from the interaction of many model components. Could we test all the potential contributing interactions?

Building understanding

HBP researchers are right: we cannot continue to study tiny pieces of the brain in isolation and hope to understand how it works. Their goal to integrate information from experiments and computer modeling is a good one. But we must build upon a solid foundation of knowledge. Markram and associated researchers have spent
the last two decades characterizing cells and mapping connections within cortex. The foundation is growing. Yet, large gaps remain. Will they be fatal to the project? Or, will HBP, as proposed, help us fill them? Neuroscientists will have to wait and see.

Recommended reading

1. “Will we ever…simulate a human brain?” by Ed Yong. BBC Future, 8 February 2013.

2. “Computer modelling: Brain in a box” by M. Mitchell Waldrop. Nature, 22 February 2012.

Acknowledgements

The author thanks Marco Herrera Valdez for feedback on earlier drafts, and Ed Yong, John Hewitt, Zen Faulkes, Philippe
Desjardins-Proulx, and Nathan Insel for valuable discussions.

Images: Fig. 1: From Saad Faruque on Flickr (license CC-BY-SA); Fig. 2: From Cajal on Tumblr (or via various sources; image in public domain); Image credit: Santiago Ramòn y Cajal, ca. 1905; Fig. 3: Modified from doi:10.1371/journal.pcbi.1000395 (original Fig. 3, license CC-BY); Image credit: Li et al. (2009), PLoS Comp. Biol., 5(5): e1000395.

Erin C. McKiernan About the Author: Erin C. McKiernan is a researcher in medical sciences and a member of the newly-formed research group for Mathematical Modeling of Micro and Macro Systems in Public Health at the National Institute of Public Health of Mexico. Her research focuses on computational problems in neuroscience, physiology, and epidemiology. The opinions expressed are those of the author and interviewees, and not necessarily those of the National Institute of Public Health or the Secretary of Health of Mexico. Follow on Twitter @emckiernan13.

The views expressed are those of the author and are not necessarily those of Scientific American.






Comments 7 Comments

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  1. 1. JPGumby 10:19 am 02/28/2013

    Those involved in this project should read “Are climate change models becoming more accurate and less reliable?”
    By Ashutosh Jogalekar | February 27, 2013 | 15 and consider the difficulties they face and the risk reduction steps they might want to try along the way.

    For example, building a complete neurologic model of a flat worm or similar creature with a simple nervous system would be a good first step. From there, more complex systems could be approached.

    Jumping straight to the human brain without a clear and well thought out series of intermediate steps is likely to be doomed to failure.

    Link to this
  2. 2. sjfone 12:23 pm 02/28/2013

    Damn, just browsing the articles it gets complicated real fast, like grass growing through Tinker Toys.
    I’m go’in back to my T.V. Guide.

    Link to this
  3. 3. Bashir 1:42 pm 02/28/2013

    Nice overview of what I think is the most interesting current challenges in science (in my biased opinion). A lot of what I read about these attempts at Brain Simulation focuses on the technical aspects of the project. Processing power and such, which is the least interesting part of this to me.

    What’s important here are the decisions the researchers have to make in this enterprise. Are we going to first attempt a 1-to-1 mapped simulation? What about a simplified model of a human, mouse or worm system? If we have to focus on some aspects of the systems which ones? What is important to model?

    This is where I think things get very tricky, subjective and interesting. We aren’t going to just wake up with a full 1-to-1 simulated brain. There are going to have to be many intermediate steps. Which direction will be fruitful, and what computational tools will help us get there?

    Link to this
  4. 4. gmperkins 3:49 pm 02/28/2013

    “If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.”
    Emerson M. Pugh, As quoted in The Biological Origin of Human Values

    As Goedel proved, a system cannot understand itself. We can at best simulate components of our brain. Understanding the whole is beyond our abilities.

    Link to this
  5. 5. jm.santi@free.fr 2:17 am 03/1/2013

    Huge project to be done but once more we spoke about neurone and how their are connecting all together; What about the gliale population and it network; Thier are connected and their are communicated together; Maybe if we try to simulate our brain it will be a good idea to study both the neuronnes network and the gliales network too. Just an idea . Jean Marc Santi

    Link to this
  6. 6. lmpereira 6:58 am 03/5/2013

    L. M. Pereira, Can we Copy the Human Brain in the Computer?
    Invited commentary slides: http://centria.di.fct.unl.pt/%7Elmp/publications/slides/brain-org/Copy%20Human%20Brain.pdf
    at “Brain.org”, Fórum Gulbenkian de Saúde, Lisboa, 9-10 October, 2012.

    Can we not Copy the Human Brain in the Computer?
    Invited commentary text: http://centria.di.fct.unl.pt/%7Elmp/publications/slides/brain-org/Commentary_Brain-Org.pdf

    Link to this
  7. 7. Torbjörn Larsson, OM 8:16 am 03/5/2013

    @gmperkins: The exact claims (several proofs) are all on _formal_ systems. Since empiricism hasn’t been formalized, and its products is a patchwork of observations and theories, the field is open. No known constraints exist, beyond the finite nature of the observable universe (so a finite state space, but also finite resources to explore it).

    Obviously neuroscience is doing great progress in understanding the system, in pieces and in whole, and we have no reason to suspect the progress will abate. But the question, which is raised specifically here, is what form progress will take.

    Link to this

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