Stop, go, pause is precisely the algorithm behind a fruit fly's brain that can be emulated on future search engines of Google, Bing and others, suggested a new study.

The scientists from the University of California and Salk Institute for Biological Studies have discovered quite an interesting resemblance between similarity searches. They found that the brain of a fruit fly conducts quite efficient and elegant similarity searches.

The brains of these insects help them identify the smells that are similar to the ones that the flies have encountered before. So, while smelling something they know whether it was safe or harmful before taking a decision to approach.

"This is a problem that pretty much every technology company with any kind of information retrieval system has to solve, so it's been something that computer scientists have studied for years. Now, we have this new approach to similarity searches thanks to the fly," said Saket Navlakha, the lead author of the study from Salk's Integrative Biology Laboratory, reported the University of California.

According to the study report, published in Science, most of the computerized data systems assign something called short "hashes" to each item that we search for. This way, similar items get assigned with the same or similar kind of hashes.

Hashes are basically digital shorthands of the larger amount of information. When a computerized system searches for similar things, instead of the original item, it looks for the hashes, so that it can find similarities faster.

The study revealed that if a fruit fly smells a pumpkin, it knows that the origin of the smell is a food item and when it smells some odor similar to that of a pumpkin, it responds in the same way towards that item; even if it smells that particular odor for the first time.

According to the study conducted by Navlakha and his team when a fruit fly first gets a whiff, 50 neurons in its brain trigger a combination, which is uniquely related to that particular smell.

However, it expands the search instead of hashing the information – it spreads the smell's data through 2,000 neurons from the first 50 so that its brain gets even more distinct a fingerprint of that smell. After that a fruit fly's brain stores only 5% of the total 2,000 neurons that mark the "top activity" or the "hash" for that odor.

Once the researchers applied this method to three usual datasets, they discovered that the fruit fly approach enhanced the similarity search performance.

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This research is significant because the scientists think that this approach is capable of informing computer systems in the near future.