Infographic: Brain-Like Computers Provide More Computer Power
Infographic: Brain-Like Computers Provide More Computer Power

Infographic: Brain-Like Computers Provide More Computer Power

Neuromorphic technology is fueling fast, large-scale simulations, supporting researchers’ endeavors to build models of the human brain.

May 1, 2019
Sandeep Ravindran

ABOVE: © istock.com, Jolygon; © istock.com, Vadim Zhakupov; Photo courtesy Unisem Europe Ltd; © HEIDELBERG UNIVERSITY; Intel Corporation; IBM Research

Neuromorphic hardware takes a page from the architecture of animal nervous systems, relaying signals via spiking that is akin to the action potentials of biological neurons. This feature allows the hardware to consume far less power and run brain simulations orders of magnitude faster than conventional chips.

THE SCIENTIST STAFF; Intel Corporation; © istock.com, Jolygon; © istock.com, Vadim Zhakupov; Photo courtesy Unisem Europe Ltd; © HEIDELBERG UNIVERSITY; Intel Corporation; IBM Research

Building a functional model of the brain

Neuromorphic technology is powering ever bigger and more-complex brain models, which had begun to reach their limits with modern super­computing. Spaun is one example. The 2.5 million–neuron model recapitulates the structure and functions of several features of the human brain to perform a variety of cognitive tasks. Much like humans, it can more easily remember a short sequence of numbers than a long sequence, and is better at remembering the first few and last few numbers than the middle numbers. While researchers have run parts of the current Spaun model on conventional hardware, neuromorphic chips will be crucial for efficiently executing larger, more-complicated versions now in development.

© Marina muun

When shown a series of numbers (1), Spaun’s visual system compresses and encodes the image of each number as a pattern of signals akin to the firing pattern of biological neurons. Information about the content of each image—the concept of the number—is then encoded as another spiking pattern (2), before it is compressed and stored in working memory along with information about the number’s position in the sequence (3). This process mimics the analysis and encoding of visual input in the visual and temporal cortices and storage in the parietal and prefrontal cortices.

When asked to recall the sequence sometime later, the model’s information decoding area decompresses each number, in turn, from the stored list (4), and the motor processing system maps the resulting concept to a motor command firing pattern (5). Finally, the motor system translates the motor command for a computer simulation of a physical arm to draw each number (6). This is akin to human recall, where the parietal cortex draws memories from storage areas of the brain such as the prefrontal cortex and translates them into behavior in the motor area.

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