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DARPA seeks to mimic in silicon the mammalian brain

The Defense Advanced Research Projects Agency (DARPA) has awarded contracts for the first phase of a program that could revolutionize computer technology and produce systems that work similar to mammalian brains.

For the military, the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) initiative could provide the foundation for machines that will supplement humans in many of the most demanding situations facing warfighters today, according to DARPA. The system would include such things as processing video images to extract information, sensory integration, robotics and decision support, the agency said.

The ultimate goal is to produce a chip that mimics the way the brain works and that would drive machines that could autonomously process information in real-world, complex environments by automatically learning what the relevant features are in those environments and how they are associated.

“If we succeed in manifesting this technology into reality, we could deploy computer systems that can deal with ambiguity and use a wide range of both biological and non-biological sensors to act the way the brain does,” said Dharmendra Modha, manager of IBM’s cognitive computing initiative.

IBM, which is working with a number of other universities in the SyNAPSE program, was recently awarded a $4.9 million contract. Other contractors are Malibu, Calif.-based HRL Laboratories, which got $6.2 million, and Hewlett Packard, which got $1.8 million.

The problem with current computers is that they require algorithms derived by humans to describe what information to look for, and then how to process that information. That’s good for situations that are relatively well-defined, but not for real-world situations where there’s an infinite number of ways in which the various data elements can interact.

In those situations, the biological brain is more efficient by factors of anywhere from one million to one billion. According to DARPA.

The revolutionary advance of a successful SyNAPSE initiative in computing terms would be to break the co-called Von Neumann bottleneck. In current computer architectures, the processor and the memory are separated and performance is limited by how fast data can be shuttled between the two.

In the brain, however, the synapse represents both memory and processing function, Modha said.

“There is no Von Neumann bottleneck in nature,” he said. “And we haven’t even begun to exploit that.”

Systems that can work like the brain have been the ultimate goal of computer research for decades, but none of the efforts have so far come to much. But there’s a confluence of three powerful trends that Modha feels could make the SyNAPSE program successful.

Neuroscience has now reached the point where it is successfully producing meaningful data about brain neurons and synapses and how they work. Also, supercomputers have progressed to where researchers can attempt ever larger simulations of brain dynamics.

Also, he said, nanotechnology has now progressed to the point where a brain’s real estate, at least at the level of some 10 billion synapses and 1 million neurons per square centimeter, could be replicated in hardware.

DARPA has designed SyNAPSE as a five-stage program that would stretch over six years or more. The final phase goal is production of a multi-chip neural system capable of driving a robotic platform that could perform at the level of a cat in terms of interpreting the environment around it, DARPA said.

However, that’s still a big if. The first stage, named Phase 0, is intended to show if the technology is even feasible. If none of the contractors succeed in that phase, which will last for up to a year, SyNAPSE will end.

About the Author

Brian Robinson is a special contributor to Defense Systems.

Reader Comments

Wed, Dec 24, 2008 wulfcry

The road ahead is never that far its the journey istelf that stals the goal not knowing what happens while your on the road. And the same is with men qeust for A.i an undefined computing factor which only purpose is to learn and adapt by-it-self. The biggest (unsound) confusion comes from mixed view of biological intelligence (humans , mammals ,insects etc) with machine intelligence (computers, electronics) two different things where one of them needs to emulate the other. It is possible to construct machine intelligence but it has not have to be in emulating biologic processes which we learn from. Its a totaly different science. The possible gain in A.i reside in programmable memory which a.i algorithms can use to update any network faster without having a black box effect. By my oppinion the memristor could be , eprom memory which almost every pc have etc. even normal cpu's with programable fpga makes possibility's. The only bottleneck in a.i is the how people feel it must be thats the error that slows the process keep it real.

Tue, Dec 9, 2008 Mark Ross Western Washington State

Yes, we're a long way from making it happen. But, isn't that how we move forward, by trying and learning from our mistakes? The first attempts at flight didn't produce much more than data about what didn't work and Edison found hundreds of ways to build an incandescent bulb that wouldn't light. But now we have airplanes and lightbulbs and ever so much more. The money being spent on this is pretty small and the data retuned may turn out to be very useful in many ways.

Mon, Dec 8, 2008 EnglishPatient Fairfax, Virginia

Is this a joke? We are so far from making this happen it's ridiculous to set such a goal. We haven't even got half-decent AI working yet. Does anyone expect this to succeed, or are we just looking for ways to get cash to cronies and send the govt further into debt in the process?

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