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  • Author Author: squadMCU
  • Date Created: 16 Nov 2011 4:21 PM Date Created
  • Last Updated Last Updated: 8 Oct 2021 4:43 AM
  • Views 643 views
  • Likes 1 like
  • Comments 3 comments
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MIT creates “brain chip”

With 400 transistors and standard CMOS manufacturing techniques, a group of MIT researchers have created the first computer chip that mimics the analog, ion-based communication in a synapse between two neurons. This is the bleeding edge of brain-like (neural network) processors, but let’s put this into perspective: The human brain has around 100 billion neurons, and each neuron can be connected to thousands of others with synapses. There are trillions or quadrillions of synapses in the human brain.

There are three ways in which you can attempt to model the human brain in silicon: You can throw more and more processing power at the problem until you reach brain-like capabilities; you can make a learning crossbar switch that simulates the multiple connections between neurons (but this gets very big very quickly); or you can go the whole hog and use analog technology to actually mimic the chemical, ion-based communication channels that flow between synapses. It is this last category which the new chip from MIT falls into.

Scientists and engineers have tried to fashion brain-like neural networks before, of course, but transistor-transistor logic is fundamentally digital — and the brain is completely analog. Neurons do not suddenly flip from “0″ to “1″ — they can occupy an almost-infinite scale of analog, in-between values. You can approximate the analog function of synapses by using fuzzy logic (and by ladling on more processors), but that approach only goes so far.
MIT’s chip  is dedicated to modeling every biological caveat in a single synapse. “We now have a way to capture each and every ionic process that’s going on in a neuron,” says Chi-Sang Poon, an MIT researcher who worked on the project. The next step? Scaling up the number of synapses and building specific parts of the brain, such as our visual processing or motor control systems. The long-term goal would be to provide bionic components that augment or replace parts of the human physiology, perhaps in blind or crippled people.

Of course, with truly analog processors comes real artificial intelligence, too — and not the kind that requires megawatts of power and a hangar full of server racks. With current state-of-the-art technology it takes hours or days to simulate a simple brain circuit. With MIT’s brain chip, the simulation is “faster than the biological system itself.”
 
http://img.mit.edu/newsoffice/images/article_images/20111114221157-0.jpg
 
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Top Comments

  • Catwell
    Catwell over 13 years ago +1
    Read about this chip along with some other brain simulation technology here . IBM is already modelling the mind, but how does it compare? How about a little satire on the subject too? See the Engineering…
  • dirtdiver
    dirtdiver over 13 years ago in reply to Former Member

    I agree! We are what we are becouse of evolution.

    And in my opinion we need to fully understand how the brain works and then try to build one.

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  • Former Member
    Former Member over 13 years ago

    and there goes so much money for a step in the wrong direction. Problem one! it is still hardware and so can not simulate biological functions that change with temperature, health, heart rate, etc. Problem two! it is still hardware and so cannot change it's base design. the brain can rearrange itself when necessary and that is also how long-term memories are stored, as new connections. whoever uses that chip only gets milliseconds of short-term memory. the reason why software based systems take so long and are a hassle is becuse they can take into an account all of these variables and more.

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  • Catwell
    Catwell over 13 years ago

    Read about this chip along with some other brain simulation technology here. IBM is already modelling the mind, but how does it compare?

     

    How about a little satire on the subject too? See the Engineering on Friday comic strip.

     

    Cabe

    http://twitter.com/Cabe_e14
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