University of California, Berkeley professor Jack Gallant and others are using machine learning to mine neuroscientific data and gain revolutionary insights into how the human brain functions.
Scientists are using artificial intelligence to analyze the data to find complex activity patterns that predict human perception, with a wealth of potential applications such as treating brain diseases.
In one experiment, a researcher in Gallant's lab demonstrated the traditional view of the brain's language-processing function is overly simplistic. He scanned participants listening to a podcast with functional magnetic resonance imaging (fMRI), while an algorithm sought patterns in the data and produced an "atlas" of where individual words correspond in the brain.
Gallant says such research can be fed into improved brain models, enabling scientists to better understand what is happening when the brain is distressed.
He notes machine learning could be used to find patterns to diagnose mental disorders, as well as to predict the onset of brain diseases so more effective therapies can be utilized.
Neuroscientists use machine learning to either encode (anticipate the pattern of brain activity generated by stimuli) or decode (predict perception by studying areas of brain activity). An example of the latter is an experiment in which a researcher rebuilt faces that participants were looking at, solely from fMRI data.
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