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NEUROSCIENCE could do for schools what biomedical research has done for healthcare. That's the conclusion of the Decade of the Mind (DOM) symposium last week in Berlin, Germany, to discuss how the latest findings could be used to improve education.

"In medicine, we have an excellent system in place to go from basic research to clinical practice, while in neuroscience we have the basic understanding of how the brain learns but still need to figure out how to translate this into the classroom," says Manfred Spitzer of the University of Ulm in Germany, one of the conference organisers. With brain imaging and, increasingly, genetic studies now complementing psychology research, a host of new findings could inform teachers about the conditions in which our brains can be primed to learn best.

"This is a potential mechanism to create the neural diversity that makes each person unique," says Gage. "The brain has 100 billion neurons with 100 trillion connections, but mobile pieces of DNA could give individual neurons a slightly different capacity from each other."

"It is known that these mobile elements are important in lower organisms, such as plants and yeast, but in mammals they are generally considered to be remnants of our past," says Gage. "Yet they are extremely abundant. Approximately 50% of the total human genome is made up of remnants of mobile elements. If this were true junk, we would be getting rid of it."

A good way to introduce the topic is to take a look at a typical application of neural networks. Many of today's document scanners for the PC come with software that performs a task known as optical character recognition (OCR). OCR software allows you to scan in a printed document and then convert the scanned image into to an electronic text format such as a Word document, enabling you to manipulate the text. In order to perform this conversion the software must analyze each group of pixels (0's and 1's) that form a letter and produce a value that corresponds to that letter. Some of the OCR software on the market use a neural network as the classification engine.

The change in what the volunteers saw was so fast, Dilks says, that it must be due to the brain redirecting signals through pre-existing circuits rather than forging new connections. The team concludes that the neurons which would normally fill the blind spot using data from the patched eye compensated by stealing data from neighbouring neurons that were "seeing" the square, making it appear like a rectangle.

Journal reference: Journal of Neuroscience, (DOI: 10.1523/jneurosci.1557-09.2009).

And he found something missing: a fourth basic circuit element besides the standard trio of resistor, capacitor and inductor. Chua dubbed it the "memristor". The only problem was that as far as Chua or anyone else could see, memristors did not actually exist.