Our brain acts as a social network
According to a U.S. professor of psychology, some regions of our brains work like digital computers. Still, our whole brains operate as social networks. Randall O'Reilly, from the University of Colorado at Boulder, says that neurons in the prefrontal cortex (PFC) are binary. They have "on" and "off" states, and act like the gates existing in our computers. O'Reilly hopes his findings will lead to a better understanding of human intelligence. Read more...
Here are two short paragraphs of a University of Colorado at Boulder news release about Randall O'Reilly's latest research work.
Digital computers operate by turning electrical signals into binary "on and off states" and flexibly manipulating these states by using switches. O'Reilly found the same operating principles in the brain.
"The neurons in the prefrontal cortex are binary -- they have two states, either active or inactive -- and the basal ganglia is essentially a big switch that allows you to dynamically turn on and off different parts of the prefrontal cortex," O'Reilly said.
Here is an illustration of these two states of neurons, which act as the digital gates in our computers. "When the gate is open, [the prefrontal cortex (PFC]) is rapidly updated with new information. When the gate is closed, it robustly maintains existing information." (Credit: Randall O'Reilly, University of Colorado at Boulder, for Science) A full caption with more scientific explanations is available from the paper linked below.
But if PFC neurons are "binary," this is not the case for our brain.
The brain as a whole operates more like a social network than a digital computer, with neurons communicating to allow learning and the creation of memory, according to O'Reilly.
However, the computer-like features of the prefrontal cortex broaden the social networks, helping the brain become more flexible in processing novel and symbolic information, O'Reilly said.
This research was published online by Science under the name "Biologically Based Computational Models of High-Level Cognition" (Volume 314, No. 5796, Pages 91-94, October 6, 2006). Here is an excerpt from the abstract.
Computer models based on the detailed biology of the brain can help us understand the myriad complexities of human cognition and intelligence. Here, we review models of the higher level aspects of human intelligence, which depend critically on the prefrontal cortex and associated subcortical areas. The picture emerging from a convergence of detailed mechanistic models and more abstract functional models represents a synthesis between analog and digital forms of computation.
And here is a link to the full paper (PDF format, 4 pages, 197 KB), from which the above image has been extracted. Here is an excerpt from the conclusions of this paper.
It is clear that the brain is much more like a social network than a digital computer, with learning, memory and processing all being performed locally through graded communication between interconnected neurons. These neurons build up strong, complex "relationships" over a long period of time; a neuron buried deep in the brain can only function by learning which of the other neurons it can trust to convey useful information.
In contrast, a digital computer functions like the post office, routing arbitrary symbolic packages between passive memory structures through a centralized processing unit, without consideration for the contents of these packages.
If you're interested in this subject, this article is only one of a special issue of Science about "Modeling the Mind." Here is a link to "Of Bytes and Brains," which summarizes this special issue.
Finally, here is a last quote from O'Reilly comparing weather and brain modeling: "Most weather models don't exactly represent what happens in a low-pressure system, but they do capture some global features," he said. "If you capture the essence of it, it tells you a lot about how the system works. It's the same premise when it comes to modeling of the brain."
Sources: University of Colorado at Boulder news release, October 5, 2006; Science, Octoer 6, 2006
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