Computational neuroscientist Guangyu Robert Yang lifts the lid on the use of machine learning to detect and process odours, and the wider implications for neuroscience.
). The study’s leader, Guangyu Robert Yang, is now principal investigator of the MetaConscious Group at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology in Cambridge. He spoke toabout the use of machine learning to process odour information, and the wider implications for the study of the brain.For the most part, it is a matter of building good sensors. There are many types in the olfactory system; the fruit fly, for example, has around 50.
After we had that basic network in place, we needed to train it to do something — and that was trickier. Engineers working on machine vision have a large library of images that they use to train neural networks. We didn’t have anything like that for olfaction, so we had to develop an artificial data set.
Once we trained the network using this data set, we found that the system evolved the neural connectivity that we had already observed in the fruit fly — it was almost the same system of signal processing at work in the animal and the machine. We were surprised by that, given how we trained the network. Such a simplified artificial data set would probably not work for vision.
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