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Scientists build the most accurate computer simulation of the brain yet

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Researchers at the University of Waterloo have built what they claim is the most accurate simulation of a functioning brain to date. Despite a seemingly unimpressive count of only 2.5 million neurons, (the human brain is estimated to have somewhere nearing 100 billion neurons), Spaun (Semantic Pointer Architecture Unified Network) is able to process visual inputs, compute answers and write them down using a robotic arm, performing feats of intelligence that up to this point had only been attributed to humans.

Save for a select few areas, our decades-old efforts in creating a true artificial intelligence have mostly come up short: while we’re slowly moving toward more accurate speech recognition, better computerized gaming opponents and “smart” personal assistants on our phones, we’re still a very long way from developing a general-purpose artificial intelligence that displays the plasticity and problem-solving capabilities of an actual brain.

The “reverse engineering” approach of attempting to understand the biology of the human brain and then build a computer that models it isn’t new; but now, thanks to the promising results of research efforts led by Prof. Chris Eliasmith, the technique could gain even more traction.

Using a supercomputer, the researchers modeled the mammalian brain in close detail, capturing its properties, overall structure and connectivity down to the very fine details of each neuron – including which neurotransmitters are used, how voltages are generated in the cell, and how they communicate – into a very large and resource-intensive computer simulation.

Then, they hardwired into the system the instructions to perform eight different tasks that involved different forms of high-level cognitive functions, such as abstraction. Tasks included handwriting recognition, answering questions, addition by counting, and even the kind of completion of symbolic patterns that often appears in intelligence tests.


Spaun was able to pass the tests on a consistent basis. “It’s not as smart as monkeys when it comes to categorization, but it’s actually smarter than monkeys when it comes to recognizing syntactic patterns, structured patterns in the input, that monkeys won’t recognize,” Eliasmith told CNN.

But in its present state, the model is still affected by some severe limitations. For one, it cannot learn new tasks, and all of its knowledge has to be hardwired beforehand. Also, Spaun’s performance isn’t exactly breathtaking: it takes the system approximately two and a half hours to produce an output that you and I could carry out in a single second.

And yet, a bit paradoxically, the most interesting results of the project aren’t Spaun’s successes, but rather its shortcomings. All in all, the model exhibited a behavior that was surprisingly similar to that of a human. When asked a question, it hesitated before answering, pausing for about as long (when correcting for performance) as a human would; when trying to memorize a series of numbers, it struggled when the list got too long and, just like a human, it remembered the first few numbers better than the ones in the middle of the list.

These similarities suggest that the system could be used to offer a coherent theory of how the brain works. Already, the researchers have used their system to show exactly how the loss of neurons – either from aging or injury – affects performance on cognitive tests. Going forward, the model could inspire promising new designs for a general-purpose artificial intelligence.

The team’s findings appear in a paper recently published in the journal Science. An open-access version of the paper is available here (PDF).

The video below illustrates the tasks that Spaun was asked to perform, and how it fared.

Sources: University of Waterloo, Nengo

Source www.gizmag.com

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