A new algorithm, called seqNMF, can detect sequences in neural data generated by ‘internal behaviors,’ such as an animal thinking or sleeping.
Listening to Mozart’s iconic “Turkish March,” it’s easy to pick out the motifs that recur throughout the piece. But identifying that string of notes would be much more challenging if the music were played through a radio with static, if it were sped up or slowed down, or if lots of notes were missing, says György Buzsáki, a neuroscientist at New York University.
That’s the task that neuroscientists are often faced with. They want to find sequences of neural activity — neurons that fire in a given pattern — that are tied to navigation, decision-making, memory and other cognitive processes. But they must sift through noisy or piecemeal data.
A new algorithm, called seqNMF, provides an efficient way to identify sequences without knowing anything in advance about the sequences themselves or the conditions that generated them. “This method allows you to extract structure from the internal life of the brain without being forced to make reference to inputs or output,” says Michale Fee, a neuroscientist at the Massachusetts Institute of Technology and investigator with the Simons Collaboration on the Global Brain. Fee’s team developed the algorithm in collaboration with that of Mark Goldman, a computational neuroscientist at the University of California Davis and SCGB investigator. “I think it will be a powerful approach for that.”