Robots with human vision in sight as scientists crack brain's 'Enigma code'
Scientists have cracked the brain's "Enigma code", paving the way for creating robots with human vision.
Researchers have deciphered what two regions of the brain say to each other when processing visual images.
Until now, scientists have only been able to tell whether two parts of the brain communicate, not what they are saying, and they have compared the breakthrough to solving the code used by Nazi Enigma encryption machines in the Second World War.
Philippe Schyns, professor of psychology at Glasgow University's Centre for Cognitive Neuroimaging, said the discovery is a huge step up in interpreting brain activity, creating opportunities to study how brain networks change with age or disease, and raising the possibility of developing "robot vision".
He said: "With Enigma, we knew the Germans were communicating, but we didn't know what they were saying. Just like if you're walking down the street and you see two people talking in the distance: you know they are communicating with each other, but you don't know what they are saying.
"Communication between brain regions has so far been like these examples: we know it's happening, but we don't know what it's about. Through our research, we have been able to 'break the code,' so to speak, and therefore glean what two parts of the brain are saying to each other."
The research could inform machine vision algorithms by revealing the information and strategies humans use when performing different challenging recognition tasks.
Prof Schyns added: "Through these discoveries, by knowing how to code and integrate these messages between different parts of the brain, we could one day give robots the same visual capabilities as people."
The researchers used a picture of Salvador Dali's Slave Market With The Disappearing Bust Of Voltaire to crack the code - focusing on the face of Voltaire and the images of the two nuns that are surreally embedded in the painting.
Lead author Dr Robin Ince said: "By randomly showing different small sections of the image, we were able to see how each part of the image affected the recorded brain signals.
"Being able to measure the content of communication between brain regions is crucial for studying the detailed function of brain networks and how, for example, that changes with ageing or disease."
The study, Tracing the Flow of Perceptual Features in an Algorithmic Brain Network, was published in Scientific Reports.