Stanford bioengineers create circuit board modeled on the human brain

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Stanford bioengineers have developed faster, more energy-efficient microchips based on the human brain – 9,000 times faster and using significantly less power than a typical PC. This offers greater possibilities for advances in robotics and a new way of understanding the brain. For instance, a chip as fast and efficient as the human brain could drive prosthetic limbs with the speed and complexity of our own actions.

Stanford bioengineers have developed a new circuit board modeled on the human brain, possibly opening up new frontiers in robotics and computing.

For all their sophistication, computers pale in comparison to the brain. The modest cortex of the mouse, for instance, operates 9,000 times faster than a personal computer simulation of its functions.

Not only is the PC slower, it takes 40,000 times more power to run, writes Kwabena Boahen, associate professor of bioengineering at Stanford, in an article for the Proceedings of the IEEE.

"From a pure energy perspective, the brain is hard to match," says Boahen, whose article surveys how "neuromorphic" researchers in the United States and Europe are using silicon and software to build electronic systems that mimic neurons and synapses.

Boahen and his team have developed Neurogrid, a circuit board consisting of 16 custom-designed "Neurocore" chips. Together these 16 chips can simulate 1 million neurons and billions of synaptic connections. The team designed these chips with power efficiency in mind. Their strategy was to enable certain synapses to share hardware circuits. The result was Neurogrid – a device about the size of an iPad that can simulate orders of magnitude more neurons and synapses than other brain mimics on the power it takes to run a tablet computer.

The National Institutes of Health funded development of this million-neuron prototype with a five-year Pioneer Award. Now Boahen stands ready for the next steps – lowering costs and creating compiler software that would enable engineers and computer scientists with no knowledge of neuroscience to solve problems – such as controlling a humanoid robot – using Neurogrid.

Its speed and low power characteristics make Neurogrid ideal for more than just modeling the human brain. Boahen is working with other Stanford scientists to develop prosthetic limbs for paralyzed people that would be controlled by a Neurocore-like chip.

"Right now, you have to know how the brain works to program one of these," said Boahen, gesturing at the $40,000 prototype board on the desk of his Stanford office. "We want to create a neurocompiler so that you would not need to know anything about synapses and neurons to able to use one of these."

Brain ferment

In his article, Boahen notes the larger context of neuromorphic research, including the European Union's Human Brain Project, which aims to simulate a human brain on a supercomputer. By contrast, the U.S. BRAIN Project – short for Brain Research through Advancing Innovative Neurotechnologies – has taken a tool-building approach by challenging scientists, including many at Stanford, to develop new kinds of tools that can read out the activity of thousands or even millions of neurons in the brain as well as write in complex patterns of activity.

Zooming from the big picture, Boahen's article focuses on two projects comparable to Neurogrid that attempt to model brain functions in silicon and/or software.

One of these efforts is IBM's SyNAPSE Project – short for Systems of Neuromorphic Adaptive Plastic Scalable Electronics. As the name implies, SyNAPSE involves a bid to redesign chips, code-named Golden Gate, to emulate the ability of neurons to make a great many synaptic connections – a feature that helps the brain solve problems on the fly. At present a Golden Gate chip consists of 256 digital neurons each equipped with 1,024 digital synaptic circuits, with IBM on track to greatly increase the numbers of neurons in the system.

Written By: Tom Abate
continue to source article at news.stanford.edu

5 COMMENTS

  1. Bio-engineering has constantly shown that designs based on those of organisms are more efficient. In my opinion this is due to the fact that organisms do not have as much energy available as we do and would therefore require energy efficient structures in order to survive.

    • In reply to #1 by 14379946:

      Bio-engineering has constantly shown that designs based on those of organisms are more efficient.

      I don’t agree with that. To begin with there are plenty of designs in organisms (the way humans breathe and eat through the same passage for example making choking a constant danger) that are very inefficient. Dawkins talks about the reasons in one of his books, that evolution is what is called in computer science a hill climbing algorithm. It can create optimal designs but it is also constrained by the nature of the problem space and the distance between optimal designs and where any organism currently falls in that space. There may be all sorts of optimal designs (eyes in the back, better support for the heads of vertebraes) that can never be achieved because the gap from where an organism currently is and an intermediary design that would still work is too far and too improbable to occur in a single mutation. That’s called the problem of local maximums.

      Also, when engineers do design they do sometimes look to nature but they almost never just blindly copy natural designs. Aeronautical engineers definitely study birds sometimes but no actual plane has ever been designed to fly the same way a bird does. Or AI researchers will study how human experts solve problems and how neural networks connect and communicate but most of the time they come up with solutions that are significantly different for machine intelligence than the way humans do it.

      I think a more accurate way to put it is to say that sometimes humans learn a lot from and can effectively mimic biological designs but they aren’t always the preferred solution by a long shot.

    • In reply to #1 by 14379946:

      Bio-engineering has constantly shown that designs based on those of organisms are more efficient. In my opinion this is due to the fact that organisms do not have as much energy available as we do and would therefore require energy efficient structures in order to survive.

      With regard to energy efficiency, I think that has been largely correct, though this may now be changing for a variety of reasons as we need to match and beat energy efficiencies as we match and exceed the same functional densities. The problem is heat removal, too.

      Its at times like these the neatness of a liquid energy delivery system that also has the thermal capacity to carry away waste heat looks quite attractive.

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