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Brain Interface Lets Monkeys Control Prosthetic Limbs
himicos was one of many readers to point out one recent success of scientists working to develop working brain-machine interfaces, writing "A team at the university of Pittsburgh has finally advanced a 2002 technology enough for use in prosthetic limbs, the targeted application all along. Training computer models to the firing patterns of the neurons in the parts of the brain that control motion, they are able to project the intentions of a monkey to a robotic arm, which follows the will of the animal.
The sad thing about the articles is that the beauty of the mathematics used to create and train the models is totally ignored." Reader phpmysqldev adds a link to coverage at the BBC, and writes "This of course brings significant hope to amputees and other other people with physical disabilities." (Note that this research has been going on for quite some time.)
Those who believe the Internet is private,
find their privates are on the Internet.
How about custom appendages? If the brain can be trained to independently control a new arm, why couldn't it learn to control a genuine Doctor Octopus suit?
It should be illegal to say that freedom of speech should be limited.
>>This of course brings significant hope to amputees
As long as they don't mind carrying a monkey to control their prosthetic arm...
Get back to me when they can use the robotic arm to fling poo.
He/she might be thinking "I wish this @#$@ing robot arm thing would quit stuffing those @%#$ed marshmallows in my mouth!"
I am amazed at the number of responses being just smug and claiming how you need to do math to appreciate the beauty. Reminds me of a guy doing PhD is Chemistry about effects of certain chiral isomer of nicotine on cancer. His first response when I asked what he worked on was "You won't get it". I am a PhD student in computational geometry and I frequently have to explain my work to relatives who have no idea about geometry. When I pestered the guy that whether or not he can explain his work to a layman reflects his understanding about his work, he agreed to try. Of course I could understand the central part once he replaced the technical name of the molecule with "a chiral isomer of nicotine". I am sure it could have been further simplified as "mirror image molecule of the stuff in tobacco" in case I didn't remember what "chiral" and "nicotine" are.
On the topic, I am not entirely sure about the exact math used in the said experiment but based on the fact that the link points to the notion of "information content", here is my guess how it should work (at least in principle). I will try just because no one else seems to. Feel free to correct me.
The state of the neurons of the relevant area of the brain (relevant for the goal in the experiment - say pick marshmallows or open the door) could be modeled as a random variable. The first problem when trying to figure out what a certain electrical activity in brain represents would be to figure out whether you are looking at a random electrical activity (brain doing lots of background work maybe) or some order (brain trying to focus and activate the subroutine for "move hand and open door"). This difference between order and chaos is captured in a neat formula describing the entropy or the information content of the random variable. Naturally, the less the entropy the more the order. I have no idea what possibly goes on after this step.
In any case, now coming to the "beauty" part. Of course you need an eye to appreciate beauty for the notion is quite subjective. The remarkable thing is that a simple formula captures the vague notion of "order" that we all have. The formula might not be the most beautiful thing because as I understood from the article, the log term is somewhat forced to make sure different things add up nicely. But then, one could think of this very fact (the extra log term) as a neat mathematical representation of the notion that disorder should be able to be combined with another disorder to create something bigger.
I hope my response is better than "drop whatever you are doing and go do a PhD in math before you can understand the beauty of math".
Shannon entropy has been a standard tool in data communications for a very long time--telcos use this math to make your phones work. It's effectively a way of quantifying the informational content of a signal, which can be used to determine exactly what kind of bandwidth you need in a bandwidth-limited environment. I'm uncertain what it's used for in the context of a brain-machine interface.
Any good data communications textbook would have some nice examples in it, and actually that wikipedia article posted is very readable and informative.