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Human brain, worms, computer chips ’similar’

London: It may come as a surprise to many, but scientists claim to have discovered striking similarities between the human brain, the nervous system of a nematode worm and computer chips. An international team, led by Cambridge University, compared the way these systems are organised and found that the same networking principles underlie all three. Using data for the large part already in the public domain, including Magnetic Resonance Imaging data from human brains, a map of the nematode worm Caenorhabditis elegans’s nervous system and a standard computer chip, they examined how the elements in each system are networked together. They found that all three shared two basic properties. Firstly, the human brain, the nematode’s nervous system and the computer chip all have a Russian doll-like architecture, with the same patterns repeating over and over again at different scales. Secondly, all three show what is known as Rentian scaling -- a rule used to describe the relationship between the number of elements in a given area and the number of links between them. "These striking similarities can probably be explained because they represent the most efficient way of wiring a complex network in a confined physical space -- be that a 3-dimensional human brain or a 2-dimensional computer chip. "Humans, worms and computer chips probably share these properties because all three evolved under then same selection pressures -- either natural selection in the case of the human and the worm, or commercial selection pressures in the case of the computer chip," lead scientist Prof Edward Bullmore said. Their research has shown that humans can learn important lessons about evolution by studying the way in which technology has developed, and by looking to simple organisms. "This challenges the commonly held belief that the human brain is special. In fact, it actually has much in common with simple organisms such as the worm and with other animal species," Prof Bullmore said. The findings have been published in the latest edition of the ’PLoS Computational Biology’ journal.

                                                                                                               
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