I have already answered most of the questions that you have raised above -- please search for my other posts (I am a researcher on the project).
As with regards to your other comments, I am willing to bet that the number of neuroscience publications produced by our team compares favorably to the number of publications of almost any group of a similar size. We know what we are doing. For example, some of us are behind the DOE/DOD project on artificial retina, to be used by blind soldiers coming home from wars. People cannot see with such retinas yet, but they can distinguish light from darkness. So, again, while we are computer geeks, we are also quite respected neuroscientists (read the team roster in the original press release and google).
The key thing, of course, that, in this project, we didn't want to simulate the real physiology (which, I agree with you, we have no hope to do in the foreseeable future). We tried to simulate the functional behavior of the network. The difference is the same as, for example, between simulation locomotion on the levelof contracting muscles and rigid bones vs. simulating gene expression and protein production in every cell in the said muscle.
I'm not too proud to ask a stupid question...
What does having this simulation on a peta-computer do that having just a super-fast computer look at something for a longer time period not do? One of the goals is to simulate the cortical processing in real time, which should almost be possible with the RR. Real time analysis allows one to process streaming video, such as from a security camera. Leaving real-time aside, there was one other reason why we needed the RR. When simulating ~billion of neurons with ~30 thousand connections per neuron, the total memory required to store the connections matrix (even if the strength of connections is calculated on the fly) is just below 100
terabytes, which is what RR has. Needless to say, if we had to store the matrix on HDDs and read/write them at every update, the calculation would take forever, not just in the proportion of the speed of the machine.
In other words... how did having a faster computer help you accomplish your goals when the challenges to this type of things are mostly software related? It's not just speed, it's RAM issues, as per above.
And if this type of processing power made you able to simulate something as complicated as vision now... wouldn't it be logical to assume even FASTER computers in the future would make it easier to create an AI -- or at least vastly better forms of intelligent systems? Seems like a straight forward extrapolation to me. Faster and bigger would be required. But even this would be insufficient. The reason we are working with vision (besides obvious practical applications), is that a lot more is known about the structure of the brain there, than anywhere else. This is largely because we know which kind of objects exist in real world, that edges are mostly smooth, that textures are only discontinuous at edges, etc. This allows one to predict, theoretically, like Steven Zucker did at Yale, what the connectivity in the visual cortex should be, up to a few global parameters, some of which we were able to fit in these first runs. I am unaware of similar arguments for other parts of the brain.
Which, of course, doesn't mean that, but the type we get bigger machines, such arguments won't be found.
In the interest of full disclosure, let me first say that I am one of the co-authors of the model that was executed on the Roadrunner, though I had nothing to do with the actual implementation that was executed (this was done by professional computer scientists, and I am a computational neuroscientist).
Let me clarify what was done, and what will be done in the future.
We simulated about 1 billion neurons communicating with each other and coupled according to theoretically derived arguments, which are broadly supported by experiments, but are a coarse approximation to them. The reason is that we are interested in principles of the neural computation, which will enable us to construct special purpose dedicated hardware for vision in the future. We are not necessarily interested in curing neurological diseases, hence we don't want to reproduce all physiological details in this simulation, but only those that, in our view, are essential to performing the visual computation. This is why we have no glia and other similar things in the model: while important in long-term changes of neuronal properties, they communicate chemically and, therefore, are too slow to help in recognition of an object in ~200 milliseconds.
The simulation was a proof of principle only. We simulated only the V1 area of the brain, and only those neurons in it that detect edges and contours in the images. But the size of V1 we simulated was much larger than in real life, so that we had only a bit smaller total number of neurons than the entire visual system in a human has. Hence we can reliably argue that we will be able to simulate the full visual cortex, almost in real time. This is what will be done in the next year or so.
When we talk about human cognitive power, we only mean the ability to look at images, segment them into objects, and recognize these objects. We are not talking about consciousness, free will, and thinking, etc. -- only visual cognition. This is also why we want to match a human, rather than to beat him: in such visual tasks, humans almost never make any errors (at least, when the images are not ambiguous), while the best computer vision programs make an error in 1 in 10 casesor so (just imagine what your life would be if you didn't see every tenth car on the road). Based mostly on theoretical arguments characterizing neuronal connectivity, and neglecting many important biological details, we may never be able to match a human (or maybe we will -- who knows? this is why it's called research). But we have good reasons to believe that these petascale simulations with biologically inspired, if not fully biological, neurons will decrease error rates by hundreds or thousands. This is also why we are content with simulating the visual system only: some theories suggest that image segmentation and object identification happens in the IT area of the visual cortex (which we plan to simulate). While the rest of the brain certainly influences its visual parts, it seems that the visual system, from the retina to IT, is sufficiently independent of the rest of the brain, so that visual cognitive tasks may be modeled by modeling the visual cortex alone.
Finally, let me add that we got some interesting scientific results from these petascale simulations and the accompanying simulations and analysis on smaller machines. But we need to verify what we found and substantially expand it before we report the results; this will have to wait till the fall, when the RR computer will be available to us again. For now, the fact that we can simulate the system the size of the visual cortex is of interest by itself.
Connection patterns in the brain are rather stereotypical; and what is not stereotypical varies from a human to another, and hence is not essential. One doesn't need to map one particular brain completely to simulate a generic visual cortex in full.
This is flawed argument. How well can we simulate the flight of a bird? Badly. But airplanes fly quite well. It's a question of what you want to simulate in the brain. More likely than not, to perform like a brain (rather than to have all the physiology of the brain), mapping the entire brain will not be needed.
Slashdot Mirror
As with regards to your other comments, I am willing to bet that the number of neuroscience publications produced by our team compares favorably to the number of publications of almost any group of a similar size. We know what we are doing. For example, some of us are behind the DOE/DOD project on artificial retina, to be used by blind soldiers coming home from wars. People cannot see with such retinas yet, but they can distinguish light from darkness. So, again, while we are computer geeks, we are also quite respected neuroscientists (read the team roster in the original press release and google).
The key thing, of course, that, in this project, we didn't want to simulate the real physiology (which, I agree with you, we have no hope to do in the foreseeable future). We tried to simulate the functional behavior of the network. The difference is the same as, for example, between simulation locomotion on the levelof contracting muscles and rigid bones vs. simulating gene expression and protein production in every cell in the said muscle.
In the interest of full disclosure, let me first say that I am one of the co-authors of the model that was executed on the Roadrunner, though I had nothing to do with the actual implementation that was executed (this was done by professional computer scientists, and I am a computational neuroscientist).
Let me clarify what was done, and what will be done in the future.
We simulated about 1 billion neurons communicating with each other and coupled according to theoretically derived arguments, which are broadly supported by experiments, but are a coarse approximation to them. The reason is that we are interested in principles of the neural computation, which will enable us to construct special purpose dedicated hardware for vision in the future. We are not necessarily interested in curing neurological diseases, hence we don't want to reproduce all physiological details in this simulation, but only those that, in our view, are essential to performing the visual computation. This is why we have no glia and other similar things in the model: while important in long-term changes of neuronal properties, they communicate chemically and, therefore, are too slow to help in recognition of an object in ~200 milliseconds.
The simulation was a proof of principle only. We simulated only the V1 area of the brain, and only those neurons in it that detect edges and contours in the images. But the size of V1 we simulated was much larger than in real life, so that we had only a bit smaller total number of neurons than the entire visual system in a human has. Hence we can reliably argue that we will be able to simulate the full visual cortex, almost in real time. This is what will be done in the next year or so.
When we talk about human cognitive power, we only mean the ability to look at images, segment them into objects, and recognize these objects. We are not talking about consciousness, free will, and thinking, etc. -- only visual cognition. This is also why we want to match a human, rather than to beat him: in such visual tasks, humans almost never make any errors (at least, when the images are not ambiguous), while the best computer vision programs make an error in 1 in 10 casesor so (just imagine what your life would be if you didn't see every tenth car on the road). Based mostly on theoretical arguments characterizing neuronal connectivity, and neglecting many important biological details, we may never be able to match a human (or maybe we will -- who knows? this is why it's called research). But we have good reasons to believe that these petascale simulations with biologically inspired, if not fully biological, neurons will decrease error rates by hundreds or thousands. This is also why we are content with simulating the visual system only: some theories suggest that image segmentation and object identification happens in the IT area of the visual cortex (which we plan to simulate). While the rest of the brain certainly influences its visual parts, it seems that the visual system, from the retina to IT, is sufficiently independent of the rest of the brain, so that visual cognitive tasks may be modeled by modeling the visual cortex alone.
Finally, let me add that we got some interesting scientific results from these petascale simulations and the accompanying simulations and analysis on smaller machines. But we need to verify what we found and substantially expand it before we report the results; this will have to wait till the fall, when the RR computer will be available to us again. For now, the fact that we can simulate the system the size of the visual cortex is of interest by itself.
That's all, folks!
Connection patterns in the brain are rather stereotypical; and what is not stereotypical varies from a human to another, and hence is not essential. One doesn't need to map one particular brain completely to simulate a generic visual cortex in full.
This is flawed argument. How well can we simulate the flight of a bird? Badly. But airplanes fly quite well. It's a question of what you want to simulate in the brain. More likely than not, to perform like a brain (rather than to have all the physiology of the brain), mapping the entire brain will not be needed.