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Mapping the Brain's Neural Network
Ponca City, We Love You writes "New technologies could soon allow scientists to generate a complete wiring diagram of a piece of brain. With an estimated 100 billion neurons and 100 trillion synapses in the human brain, creating an all-encompassing map of even a small chunk is a daunting task. Only one organism's complete wiring diagram now exists: that of the microscopic worm C. elegans, which contains a mere 302 neurons. The C. elegans mapping effort took more than a decade to complete. Research teams at MIT and at Heidelberg in Germany are experimenting with different approaches to speed up the process of mapping neural connections. The Germans start with a small block of brain tissue and bounce electrons off the top of the block to generate a cross-sectional picture of the nerve fibers. They then take a very thin slice, 30 nanometers, off the top of the block. 'Repeat this [process] thousands of times, and you can make your way through maybe the whole fly brain,' says the lead researcher. They are training an artificial neural network to emulate the human process of tracing neural connections to speed the process about 100- to 1000-fold. They estimate that they need a further factor of a million to analyze useful chunks of the human brain in reasonable times."
Not to mention - and I a sure I'll get modded down here - is that neural networks aren't very effective. They get a lot of hype in the media, and people who don't work in optimization like them. They sound appealing and cool, but there are other methods that are much better. If you look at the liturature, there is not that much hard science behind them. Some statistical mechanics people have some results, but they really are just a fad, like fractals or catastrophe theory. But bear in mind LOTS of people in applied areas immediately jump to the conclusion that neural networks are great, especially computer vision and robotics people (where I used to work.)
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An attempt to emulate a brain on a network of computers.
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That's what they call the brain's ability to change. By the time they complete a wiring diagram, it'll have changed. Also, knowing the wiring and connections is not enough. Knowing which connections are excitatory and which are inhibitory is necessary, and then tracking down loops of excitatory against excitatory resulting in inhibitory, etc. It's all fine and well to have a map, but that doesn't tell you squat about what anything does. A useful map would have to be dynamic, and the complexity of that is far more than just what they're considering for a wiring diagram.
"I may be synthetic, but I'm not stupid." -- Bishop 341-B
Many engineers have studies feed-forward neural networks and found them to be far inferior to other solutions. Of course, our brains use recurrent neural networks, which, unfortunately for engineers, are very difficult to analyze. There are many secrets yet to be teased out of these neural networks, but much progress has already been made. Researchers in our lab, for example, have demonstrated how introducing random synaptic failures improves not only energy efficiencies, but also the cognitive abilities of simulated neural networks. I'm currently researching the effects of variable activity (as measured in a biological neural network by an EEG), and I dare say there's a lot more that we don't know about these networks than we do.
Ben Hocking
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Hell, it will be fun to see what they figure out to be the capacity of the brain, as far as how much information it can store.
I know visually we are looking at least at something around 24 frames per second. The eye is supposed to have a resolution of around 1000 dpi. Not sure how to measure the viewing area. But let's say it is lesser and lesser resolution the higher the angle. Let's say, just to have a number, that we have a 16:9 viewing ratio at two feet distance. Lets say it's three feet wide. That should add up to something around 36"x20". At 1000dpi that would be something like 729,000 dots. Time 24 per second becomes 17,496,000 dots per second.
Though I think people who have dissected eyes and the stem to the brain would have a hard time quite understanding how that dpi reaches the brain.
On a daily basis, if we don't count 8 hours sleeping, we still come up with 280 million pieces of data in 16 hours.
If an average brain is 1400 cubic centimeters or 85 cubic inches, how many cells could it have if we say it is solid. Best case scenario.
I see numbers of 9,350 cells per cubed millimeter which is 93,500 per cube centimeter. With the above brain size we are looking at 130,900,000 cells in a solid brain.
To complicate things further, how many days of memory do you have? Most people have problems remembering all details but then some people have photographic memory. Which as far as I can see means that all of us has the potential to have it.
The running question is how much info is stored in each cell.
Of course that's not including all the other senses and impressions that are stored.
and it's only like 302 neurons,so,it's possible to write a simulator of it?
Our computer technologies have yet to achieve the complexity of most biological brains. I'd love to see these new informations derive a new form of super-computer. Of course...We have to watch out for iRobot scenarios...
Don't hold your breath for an iRobot.
If each of the 100 billion neurons managed the 1000 or so synapses, and say a modern day PC with a quad processor could computationally handle say 100 neurons, you would need 1 billion PCs. Since 1 billion PCs would find it difficult to walk, the old adage of: "computers are as dumb as nails" still applies.
Now lets say usable processing power doubles every 5 years, and it shrinks to something small enough it can walk into our living rooms. That would be at least 150 years from now.
You could argue Moore's law is faster, but 2 issues. First is can it continue, not likely. Second is can we has humans program something this complex, not likely. Each generation of computers we get less and less value out of for the increases in CPU. You could say hello in 10-12 bytes in the '70's and early 80's, now it takes well over 1,000,000 bytes and 20 times longer to do the same thing on today's computers. Go figure.
So iRobot, Cherry 2000 and Commander Data are going to have to wait quite some time long after our great-great-great-great-great-great-great-great-great-great-great-great grandchildren have passed on. Say half the time to get a muppet.
Yours is the best comment yet on this thread. I did NN stuff at U.Massachusetts/Amherst 1973-1974 when NN was temporarily un-cool, due to the Minsky-Papert Perceptrons book. I shifted to genetic algorithms 1974-1977 while beta-testing John Holland's book. One conclusion I reached was that memory and cognition are nanomolecular processes, and the neural network is merely the Local Area Net that connects distant regions of nancomputers.
-- Prof. Jonathan Vos Post