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Building a Silicon Brain

Posted by kdawson on Tue Feb 13, 2007 12:21 AM
from the million-neuromimes dept.
Science Technology
prostoalex tips us to an article in MIT's Technology Review on a Stanford scientist's plan to replicate the processes inside the human brain with silicon. Quoting: "Kwabena Boahen, a neuroengineer at Stanford University, is planning the most ambitious neuromorphic project to date: creating a silicon model of the cortex. The first-generation design will be composed of a circuit board with 16 chips, each containing a 256-by-256 array of silicon neurons. Groups of neurons can be set to have different electrical properties, mimicking different types of cells in the cortex. Engineers can also program specific connections between the cells to model the architecture in different parts of the cortex."
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  • obligatory (Score:5, Funny)

    by intthis (525681) on Tuesday February 13 2007, @12:23AM (#17993524)
    that's great, but will it run linux?
  • [pinky finger]

    Bet you could train that to do some cool stuff.. assuming it runs in realtime, as advertised, and what kind of back-propagation algorithms are implemented?

    Neat though.

    • Re:One Million Neurons ;) (Score:4, Informative)

      by tehdaemon (753808) on Tuesday February 13 2007, @12:39AM (#17993672)

      As far as I know, brains do not use back-propagation at all. Each neuron changes it's own weights based on things like timing of inputs vs output, and various neurotransmitters present.

      If all you want are more neural nets like we have been doing then sure - back-propagation algorithms matter. That does not seem to be the goal here though.

      T

      • Re: (Score:2)

        by QuantumG (50515) *
        meh, back-propagation is a mathematical simplification of neurotransmitters. You really think these silicon neurons are anything other than mathematical simplifications of organic neurons?

        • Re: (Score:3, Interesting)

          by tehdaemon (753808)

          back-propagation is a mathematical simplification of neurotransmitters.

          No. Correct me if I am wrong, but back-propagation works by comparing the output of the whole net to the desired output, and tweeking the weights one layer at a time back up the net. In real brains, neurotransmitters either do not travel up the chain more than one neuron, or they simply signal all neurons physically close, whether they are connected by synapses or not. (like a hormone) Further, since real brains are recurrent networks (

          • Re: (Score:2, Interesting)

            by Triynko (1062726)
            Yeah, back propagation has little to do with brain circuitry. After reading extensively on neurons and their chemical gates and wiring, it's pretty obviously that basic neural networks that have been implemented look nothing at all like the brain.

            The brain learns by weakening existing connections, not by adding new ones. It's logically and physiologically impossible for the brain to know in advance which connections to make in order to store something... it's more of a selection process. This is also w

            • Re: (Score:3, Insightful)

              by Rei (128717)
              That's not really true. There are many, many different approaches out there. None have hit that "sweet spot" that we're looking for. Some abstract away from the brain heavily using mathematical representations in order to get more performance, effectively simulating more neurons. Others go with a more physical representation, but consequently accept poorer performance. Obviously, there's a good bit of variation on the mathematical models, but there also are variations on the physical models -- what do

    • Re: (Score:2, Funny)

      by skeftomai (1057866)
      Would this thing do parallel processing?

      • Re:Depends on What Consciousness Is (Score:4, Insightful)

        by QuantumG (50515) * <qg@biodome.org> on Tuesday February 13 2007, @01:37AM (#17994036) Homepage Journal
        I, like many other engineers, don't give a shit. We just want to solve problems to which there are no simple solutions and "AI" offers some approaches that work.

        Leave the philosophy till after we have the science.

      • Re:Depends on What Consciousness Is (Score:5, Insightful)

        by Venik (915777) on Tuesday February 13 2007, @03:07AM (#17994472)
        How can you build a software model of a process you don't understand? The best hope is to build a hardware approximation of a human brain and hope that, somehow, the same processes start occurring, quantum or otherwise. And if that doesn't work, then you'll have to do some real science.

        • Re: (Score:3, Insightful)

          by vertinox (846076)
          How can you build a software model of a process you don't understand?

          Same way the Wright Brothers built their first aircraft.

          1. Make observations of things that do fly.
          2. Make an approximation of what it takes to fly based off those observations.
          3. Build a model based off that.
          4. See if it works in a trial run.
          5. If it doesn't, back to step one.

          Obviously, the Wright Brothers understood basic aerodynamics, but only at a certain level from observations of test gliders and the semi-wind tunnel setup they had b

          • Re: (Score:3, Insightful)

            by joto (134244)
            I'm sorry, I don't have 4.54 billion years to spend on something that might produce similar results. Besides, there's no guarantee we would understand that either. Could you suggest something faster and/or better/more predictable?

          • Re: (Score:3, Insightful)

            by TheLink (130905)
            Uh if you are going to resort to getting an intelligent creature without understanding how it was done, you might as well get one from a petshop.

            Sure animals have disadvantages but how sure are you that the AI you get after doing that "evolve it" thing won't have similar disadvantages too?

      • Re:Depends on What Consciousness Is (Score:5, Informative)

        by kestasjk (933987) * on Tuesday February 13 2007, @04:14AM (#17994816) Homepage
        Read "How Brains Think" by William H. Calvin; he's a neurologist and the book goes into lots of detail about how brains think (dur), how they evolved, and the possibility of AI.
        He's an expert in the field and you can feel his bitter dislike of "quantum consciousness" proponents through his writing. He writes that it's just saying "we don't know how X works, and we don't know how Y works, but if we say that Y depends upon X then we have one problem instead of two".

        Consciousness is built on the interactions of neurons. We understand how neurons work at interact at a low level (from studying the ~50 neuron brains of snails etc), and we understand on a large level which regions of the brain do what, but we don't understand the "middle ground".

        It's as if we understand the transistor, and logic gates, and we can recognize which part of a chip is the ALU and which is the cache, but we can't recognize an adder circuit or microinstruction translator for what it is.

        Quantum physics is certainly involved in the action of transistors but it doesn't explain how they combine to process data.

        (On a similar note some I saw, in a documentary, one crackpot explain away "spontaneous human combustion" with an unknown quantum particle.)

  • so... (Score:4, Funny)

    by President_Camacho (1063384) on Tuesday February 13 2007, @12:26AM (#17993562) Homepage
    prostoalex tips us to an article in MIT's Technology Review on a Stanford scientist's plan to replicate the processes inside the human brain with silicon.

    So how long until we get AI that's addicted to World of Warcraft?

  • 2^^20 neurons? That's wayyyy too many (Score:3, Funny)

    by Eternal Vigilance (573501) on Tuesday February 13 2007, @12:32AM (#17993624)
    ...to accurately model most American thought processes.

    Gotta go - American Idol's back on.


    Dave, my mind is going. I can feel it...

  • Go to Hollywood (Score:3, Funny)

    by syousef (465911) on Tuesday February 13 2007, @12:36AM (#17993654) Journal
    Lots of silion in Hollywood....oh you said BRAINS not BREASTS.

  • Hardly something new... (Score:5, Interesting)

    by Anonymous Coward on Tuesday February 13 2007, @12:49AM (#17993726)
    This is hardly something new. Intel had a chip a number of years ago, called ETANN that was a pure-analog neural network implementation. Another cool aspect of this chip was that the weight values were stored in EEPROM-like cells (but analog) so the training of the chip would not be erased if it lost power.

    But the whole technology of neural networks almost pre-dates the Von Neumann architecture. Early analog neural networks were constructed in the late 40's.

    Not only are these simulations nothing new but they are in every-day products. One of the most common examples is the misfire detection mechanism in Ford vehicle engine controllers. Misfire detection in spark ignition engines is based on so many variables that neural networks often perform better than hard-coded logic (although not always, just like the wetware counterparts, they can be "temperamental").

    There are several other real-world neural network applications (autofocusing of cameras for example).

    Ahh the hidden magic of embedded systems...

        • Re: (Score:3, Interesting)

          by TheLink (130905)
          You are assuming that the brain is just one implementation of a computer.

          And even if it is true, if it's only true in the way that "The universe is just one implementation of a computer" then I don't think that teaches us that much about the brain/mind (it will still teach us something of course).

          Don't get me wrong though, I do agree that computer science and information theory are fundamental sciences.

          And I also agree with you that the first AI wouldn't be a model of the brain.

          I'm no neuroscientist or comp
  • What the heck do you put in the boot ROM for this kind of thing?

  • This is the most ambitions??? What about Markram & IBM [forbes.com]? They must be just fooling around with that Blue Gene (actually I do think they are fooling around, but that's beside the point). What about Izhikvich [nsi.edu]? He simulated just a puny 100 billion neurons. That's *nothing* compare to this "most ambitious" million.

      • Re: (Score:2)

        by wanax (46819)
        Would they though? Izhikevich has taken a lot of time to try to get neurons into a 'reasonable' computational size, using a bunch of tricks from dynamical systems. This system may approach those dynamics, but it wasn't clear from the article. But that still isn't a general neuronal model. 'Regular' pyramidal cells often receive input from ~10-20k other cells, and there's no general description of which have an 'active' dendritic tree (ie. one that has areas that can spike towards the soma). There are plenty
  • The study of the brain is one of the youngest sciences in terms of what we know... But from my experience, the people in this field realize that even rough virtualization of the brain won't happen for a long, long time. Why these people are so optimistic is beyond me.

    But maybe I'll eat my words. Doubtful.

    • Since the experts know so little, maybe we shouldn't put so much on weight on their words?

  • What'll be new? (Score:5, Informative)

    by wanax (46819) on Tuesday February 13 2007, @01:18AM (#17993916)
    I have to wonder what the purpose is.. You can model simplified 'point' neurons, and various aggregates that can be drawn from them (eg, McLoughlin's PDEs)... or you can run a simplified temporal dynamic (eg. Grossberg's 3D LAMINART), and easily include 200k+ neurons in the model easily to capture a broad range of function. For those would like running more detailed models of individual neuronal dynamics, you have Markram's project simulating a cortical column with compartmental models, or what Izhikevich is doing with delayed dynamic models.

    Although this setup may be able to run ~1mil neurons, in total, it would seem that with 16 chips of 256x256 each, the level of interaction would be limited, and the article has no indication that these are the more complicated (and realistic) compartmental models of neurons that can sustain realistic individual neuronal dynamics (and for example Izhikevich, Markram and McLoughlin have spent a lot of time trying to simplify), or whether this is just running point style neurons a bit faster than is traditional.. and I have to wonder here, whether if these chips can't do compartmental models, why not just run this on a GPU?

    I checked out this guy's webpage, and he seems smart.. but this project is years away from contributing.. I wonder, especially with the Poggio paper yesterday, when the best work being done just at MIT in Neuro/AI right now is probably in the Torralba lab, whether slashdot editors may want to find some people to vet the science submissions just a tad.
  • I was under the impression neurons used neurotransmitters to communicate info between two cells but this article implies electrical signals do that. It would be nice to read some text on this subject that tried to explain the abstract difference between what transmits what information.
  • I, for one, welcome our new silicon-brain overlords.

  • About the only thing impressive about 1 million neurons is that it is slightly more than the square root of the number of neurons in the human brain.

    Wake me up after the exponential growth has been going on a little while longer and they have made up the 6 orders of magnitude they need to make it worth of the term "brain".

  • Naturally Intelligent Systems (Score:5, Interesting)

    by TheCouchPotatoFamine (628797) on Tuesday February 13 2007, @01:43AM (#17994066)
    For those interested in this field, may i suggest a book, Naturally Intelligent Systems? It's slightly older, but it explains a wide gamut of neural networks without a single equation, and manages to be funny and engaging at the same time. it is one of the three books that changed my life (by it's content and ideas alone - i'm not otherwise into AI). highly recommended: Naturally Intelligent Systems on amazon [amazon.com]
  • Why would you experiment with neural logic in hardware when software is infinitely scalable and programmable and arguably more valuable in the reserch of neural networks? Of course software is a degree slower in response time, but speed is not of the essence for researching the "how" of neural nets.

    I would think that in the hardware world, generally you would want a working software model and then duplicate it with the more expensive hardware for performance. The same principal applies when ASIC engineers

  • Why? (Score:3, Funny)

    by Quiet_Desperation (858215) on Tuesday February 13 2007, @03:05AM (#17994466)
    Look at the rubbish the human brain generates. Ideology. Irrationality. Depression. Religion. Politics. Reality TV.

    You really want processors that need weekly visits from an Eliza program and iZoloft patches?

    "Sorry, Bob. I can't run those projections now. The supercomputing cluster is in a funk over the American Idol results."

    Y'all think AI is going to be so great and a bag of chips, too.
    • The human brain generates so much rubbish because it does not use mathematical logic, but pattern matching.

      In many cases, mathematical logic can not be used to prove the absolute truth of a proposition; therefore the brain uses pattern matching to 'prove' the 'truth' of a proposition to the degree that is useful for the survival of the entity that carries it.

      Take, for example, the proposition that 'prime numbers are infinite'. We all think they are infinite, but there is no mathematical proof for it yet. Wh
      • Take, for example, the proposition that 'prime numbers are infinite'. We all think they are infinite, but there is no mathematical proof for it yet.

        There have has been a proof for it for a long time. Gettin' wiki [wikipedia.org] wit it.

        Quoting from the link:

        There are infinitely many prime numbers

        The oldest known proof for the statement that there are infinitely many prime numbers is given by the Greek mathematician Euclid in his Elements (Book IX, Proposition 20). Euclid states the result as "there are more than any given

  • More than just modeling the brain (Score:4, Insightful)

    by AndOne (815855) on Tuesday February 13 2007, @03:34AM (#17994608)
    Having been a fan of neuromorphic engineering for several years now(Note I'm not an active researcher but I pretend somedays :) ) one of the major advantages of neuromorphic functionality isn't necessarily it's ability to model biological systems but the fact that the devices are extremely low power. When modeling neurons in silicon(at least back in the day of Carver Mead's work and for cochlea and retina stuff and I'm doubting it's changed too bunch but I could be wrong) the transistors would run in sub threshhold mode(basically leakage currents so OFF) since the power curves modeled the expected neuro response curves. One of Boahen's stated goals(at least on his website when he was at Penn) was to reduce power consumption and improve processing power for problem solving via these techniques. His lab has been in Scientific America a couple times in the last few years for work in accurately modeling Neuronal spiking in hardware too. I have them but not at hand so I can't cite them at the moment but they were fun reads.

    So in summary, it's more than just modeling the brain. It's about letting biology inspire us to make better and more efficient computing systems.

  • Paging (Score:3, Interesting)

    by mdsolar (1045926) on Tuesday February 13 2007, @07:55AM (#17995874) Homepage Journal
    The article says that the chip will work at 300 teraflops. The human brain might be rated at 100,000 teraflops http://www.setiai.com/archives/000035.html [setiai.com] so there is still quite a lot of speed to make up. However, it seems to me that through state saving (paging) one could simulate the connections between many more that a million neurons using this device. If you virtualize as a cube 3000 deep and track connections between these layers in software then processing over the virtual layers can proceed sequentially. So, it seems as though it won't take all that much more hardware development to get to simulations on the human scale owing to the higher frequency of individual operations.
    --
    Solar, a bright idea http://mdsolar.blogspot.com/2007/01/slashdot-users -selling-solar.html [blogspot.com]

  • Here Here! (Score:3, Funny)

    by LifesABeach (234436) on Tuesday February 13 2007, @09:05AM (#17996368)
    Having hardware that duplicates human thought is an excellent corner stone to help me with my many woes. With Hard Drives approaching the Pico byte range, we will be able to backup our memories; And access vitally important past events. Obviously, there will be many more steps to take before I will be able to access things like my wifes birthday, our first date, and so on. Personally, I will be very grateful for less arguments about past events that I have for some reason or another, considered to trivial to remember.

    "Come back Dear! I'm good with True-False!" - Larry, the Cable Guy

  • I don't get it. (Score:3, Informative)

    by God of Lemmings (455435) on Tuesday February 13 2007, @09:32PM (#18007020)
    This article tells us absolutely nothing about the design other than that the
    total number of neurons emulated is very small. And no, this is not the "most
    ambitious project yet" by a landslide. It is dwarfed by IBM's own Blue brain project, as well
    as CCortex.

    http://en.wikipedia.org/wiki/Blue_Brain [wikipedia.org]

    The only novelty I see here is that they fabricated artificial neurons on a chip, which greatly
    speeds up the whole thing.

    • The reverse seems more interesting. (Score:4, Interesting)

      by Kadin2048 (468275) <slashdot@kadin.xoxy@net> on Tuesday February 13 2007, @12:46AM (#17993708) Homepage Journal
      One thing you don't hear much about, is what progress, if any, is being made in interfacing electronic systems into biologic ones, and growing biologic circuits. Perhaps our understanding of biological computation and storage simply isn't complete enough to make such a system practical, even if we were able to somehow interface a clump of neurons to the outside world electronically, but it certainly seems like the data storage capacity of biologic systems is far greater (per mass/volume) than anything devised artificially. Although, I suppose it's impossible to equate, since it's not clear how 'compressed' information is, when it's encoded by the mammalian brain as memories.

      • Re: (Score:3, Interesting)

        by QuantumG (50515) *
        I thought Interface [wikipedia.org] was a remotely interesting read.. at least the technological aspects.. the commentary on media dominated elections was just depressing. They extract some neural tissue from a subject, grow a bunch of neurons, interface them to chip with a wireless transmitter, then reinsert them into the brain. Then, with some training, the chip can replace functions of the brain destroyed by stroke or cancer or whatever. The data dump of the communication between the neurons and the chip is the reall

      • Re: (Score:3, Insightful)

        by NixieBunny (859050)
        An interesting aspect of the brain is that it may be possible to build circuitry that mimics its behavior without understanding that behavior. There are many complex systems (collections of simple parts) that exhibit surprisingly coherent behavior that you just wouldn't expect. Swarms of locusts are one example. The insect robots that learn how to walk every time you turn on their power is another example.

    • Re:Only one mibiNeuron? (Score:4, Insightful)

      by tehdaemon (753808) on Tuesday February 13 2007, @12:48AM (#17993716)
      Are you sure about that? FTA:

      "We can currently do small simulations of hundreds to thousands of neurons, but it would be great to be able to scale that up," he says.

      A 2.0GHz dual-core CPU running 2^20 neurons in the net at 100Hz gets about 40 clock cycles per neuron per cycle...Somebody check my math please.

      T

      • BOINC (Score:4, Insightful)

        by Gary W. Longsine (124661) on Tuesday February 13 2007, @01:01AM (#17993804) Homepage Journal
        I would think a BOINC project might produce enough muscle to get a really big brain going. Imagine a BOINC [berkeley.edu] cluster of...

        ;-)

      • Re: (Score:3, Insightful)

        by wall0159 (881759)

        The calculations involve adjusting the weight of connections between neurons, which generally scale exponentially with the number of neurons. This is because each neuron typically has connections to many other neurons.

        So, your math might be right, but your assumptions are wrong. :-)

      • Re:Only one mibiNeuron? (Score:4, Informative)

        by naoursla (99850) on Tuesday February 13 2007, @03:48AM (#17994686) Homepage Journal
        Now add a bunch of connections between all of those neurons. As you approach fully connecting the network, the time complexity to compute one time-step approaches O(N^2) where N is the number of neurons.

        2^20 * 2^20 == 2^40. Ignore memory cache constraints for a moment and say each update takes 1 clock cycle. Since we are dual core we can get 2 updates per cycle. Each clock cycles takes 500pS. 2^40*500ps/2 means each complete brain update takes 274s on your computer.
    • Meanwhile, at Beowulf Art School, 4 students are working on an animation project, each of them drawing 1/4 of each frame.

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