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The key component of the computer "brain" under development by IBM and the government is the modeling of synapse connections between neurons.  (Source: Science Photo Library)
IBM is trying to develop circuits that mimic the human brain

Think that computer upgrades could one day make the human brain obsolete?  You're not alone.  However, to reach that critical milestone key upgrades in computing will be needed to make computers more brain-like in operation.

To those ends IBM is taking the lead on a major government research endeavor in the field of "cognitive computing" which pairs neurobiologists, computer and materials scientists and psychologists in a $4.9M USD DARPA-grant driven project to develop a computer that behaves like a brain, down to the neuron level.

Dharmendra Modha, the IBM scientist who is heading the collaboration describes, "The mind has an amazing ability to integrate ambiguous information across the senses, and it can effortlessly create the categories of time, space, object, and interrelationship from the sensory data.  There are no computers that can even remotely approach the remarkable feats the mind performs.  The key idea of cognitive computing is to engineer mind-like intelligent machines by reverse engineering the structure, dynamics, function and behavior of the brain."

The project will utilize an IBM supercomputer as its hardware, a field where IBM has long been king of the hill.  Five universities will devote their talents to making this computer behave like a collection of neurons.  The goal is replicate behavior in simulations.  The long term goal is to create a "brain" on the intelligence level of a cat.

The work will draw heavily from neuroscience, which has mapped out simple animal brains and how they respond to stimuli.  Project leader Mr. Modha has some brain-simulating experience of his own -- last year he led a team which used an IBM BlueGene supercomputer to simulate a mouse brain with 55m neurons and some half a trillion synapses.  He describes, "But the real challenge is then to manifest what will be learned from future simulations into real electronic devices - nanotechnology."

Today electronics can be manufactured at as high a density as animal neurons.

The new effort differs from efforts to establish so-called neural networks.  Neural networks, which seek to simulate connections of neurons and can approach learning-like behavior, and artificial intelligence are inherently different from the attempt to create a full brain.  Says Mr. Modha, "The issue with neural networks and artificial intelligence is that they seek to engineer limited cognitive functionalities one at a time. They start with an objective and devise an algorithm to achieve it.  We are attempting a 180 degree shift in perspective: seeking an algorithm first, problems second. We are investigating core micro- and macro-circuits of the brain that can be used for a wide variety of functionalities."

The result is more of a synaptic network than a neural one.  The key component to which the brain owes its flexibility is the synapse.  Synapses connect neurons together in the brain and it is these connections that help us think. 

Experts worldwide are intrigued by the project, but fear that the US government is underfunding it.  Still, says Christian Keysers, director of the neuroimaging center at University Medical Centre Groningen, "It's an interesting effort, and modeling computers after the human brain is promising."


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Back to the Future
By nah on 11/21/2008 12:50:38 PM , Rating: 2
quote:
From the general philosophical standpoint, it is of interest to compare the the most complicated machine we can make with the machines we observe in nature, such as the human central nervous system. Warren McCullough of the University of Illinois Medical School has done this in very entertaining terms. He finds that the Eniac computer, containing about 10,000 basic-on-or-off elements, is a million times less complex than the brain, which has 10,000 million neurons. The Eniac, indeed, has about the complexity of the nervous system of the flatworm. It has one advantage : its unit operations are accomplished about a thousand times faster than are the unit operations of the brain. Thus if we made a sort of figure of merit for comparing the competence of man-made and natural machines, taking into account both complexity and speed, we should find the Eniac---for those operations fitted to its very low complexity--only about a thousand times less competent than the human brain.

McCullough remarks that if we made a vacuum-tube computer as complex as the brain,it would require a skyscraper to house it, the power of Niagara to operate it and the full flow of water over the falls to keep it cool. This is altogether a criticism of vacuum tubes.If, as seems reasonable to suppose, the use of transistors will permit a further hundred-fold increase in the complexity of our machines, we shall be able to build, in no greater space and with smaller power requirements than are needed now for vacuum-tube computers, a device only 10,000 times less complicated than the brain. Since it will work a thousand times faster, such a transistor device may be, for those jobs to which its low complexity suits it, as much as one-tenth as competent as the human brain.

This is an exciting prospect,but it has not yet been achieved.Curiously, its achievement seems to rest on the elimination from electronics of the vacuum tubes which gave electronics birth.

--Louis N. Ridenour Scientific American ( August 1951)




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