Image of the Day: “The most complex mass of protoplasm on Earth—perhaps even in our Galaxy.”


“We have successfully uncovered and mapped the most comprehensive long-distance network of the Macaque monkey brain, which is essential for understanding the brain’s behavior, complexity, dynamics and computation,” announced Dharmendra S. Modha of IBM.

The scientists focused on the long-distance network of 383 brain regions and 6,602 long-distance brain connections that travel through the brain’s white matter, which are like the “interstate highways” between far-flung brain regions, he explained, while short-distance gray matter connections constitute “local roads” within a brain region and its sub-structures.

“We studied four times the number of brain regions and have compiled nearly three times the number of connections when compared to the largest previous endeavor,” he pointed out. “Our data may open up entirely new ways of analyzing, understanding, and, eventually, imitating the network architecture of the brain, which according to Marian C. Diamond and Arnold B. Scheibel is “the most complex mass of protoplasm on earth—perhaps even in our galaxy.”

The brain network they found contains a “tightly integrated core that might be at the heart of higher cognition and even consciousness … and may be a key to the age-old question of how the mind arises from the brain.” The core spans parts of premotor cortex, prefrontal cortex, temporal lobe, parietal lobe, thalamus, basal ganglia, cingulate cortex, insula, and visual cortex.

This discovery aligns remarkably well with three decades of behavioral imaging studies that “exhibit a ‘task-positive’ network implicated in goal-directed performance and a ‘task-negative’ network activated when the brain is withdrawn and at wakeful rest,” they point out.

Similar to how search engines uncover highly ranked web-pages using network theory, by ranking brain regions, they found evidence that the prefrontal cortex is a topologically central part of the brain that might act as an integrator and distributor of information.

Modha and  Raghavendra Singh (IBM Research-India) also found that the brain network does not appear to be “scale-free” like the web’s social networks, which are logical and can grow without constraints, but seems to be ”exponential,” like air traffic networks, which are physical and must satisfy resource constraints. “This finding will help us design the routing architecture for a network of cognitive computing chips,” they suggest.

“The network opens the door to the application of large-scale network-theoretic analysis that has been so successful in understanding the Internet, metabolic networks, protein interaction networks, various social networks, and in searching the world-wide web," they added. "The network will be an indispensable foundation for clinical, systems, cognitive, and computational neurosciences as well as cognitive computing.”

Casey Kazan


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