The Most Computer Simulation Of The Human Brain

Brain 090407

The human brain (Photo credit: Wikipedia)

The Canadian University of Waterloo researchers have announced that they have reached for the most computer simulation of the human brain. The simulation is the most accurate ever reached as the computer can see the inputs and process it and then write it using a computer arm. They called it Spaun (Semantic Pointer Architecture Unified Network).

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.


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.

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