Statistics Predict How Birds Will Chirp Next

Statistics Predict How Birds Will Chirp Next

Physicists have developed a model that can map out and predict the notes birds sing in sequence. Here’s how it works.

The new model is roughly eight times more accurate than previous attempts to unravel complex birdsongs. If the scientists can use the same technique to map and predict chatter in other social animals, they could find important clues about the neural origins of complex language, including that of humans.

“We want to gain an understanding of this simplest case, then work our way up in complexity,” said physicist Dezhe Jin of Penn State University who led the research, posted November 12 on

Birdsong originates at the top of a bird’s brain in an area called the HVC, or higher vocal centre, which is made up of about 40,000 neurons. Networks of thousands of individual neurons there are thought to generate syllables, and these neural networks link up to other areas of the brain to actually vocalise the sounds.

Mapping the sounds and their sequence in a song may help resolve such language-centric brain pathways.

“We think it’s like a domino effect, where one syllable cascades into the next to create complex songs,” Jin said. “But before neural coordinates can be verified, we need to have robust statistical maps.”

Jin stuck a Bengalese finch in a soundproof room for six days with a microphone. The bird tweeted more than 25,000 times, sounds which Jin and his team divvied up into 25 groups based on statistical similarity. In total, the finch sang seven distinct song syllables (sounds made very quickly one after the other) and 14 other types of notes.

Unlike previous models, which skyrocket in error when trying to predict more than one note in sequence, the new model factors in the order of previous notes. It also takes into account the fact that different neural networks may produce the same syllable which, Jin says, provides a subtle but crucial detail in correctly mapping and predicting a song’s syllables.

No model will ever be able to predict a bird’s song with 100 per cent accuracy because they improvise as they go, like jazz musicians, Jin said. But they may be able to get close enough to begin to understand what’s happening in the bird’s brain.

“This is really the beginning of finding how song and language structure originates,” Jin says. “We want to further study other species and apply that knowledge to humans.”

Images: 1) Spectrograms of song notes (top / a), call notes (middle / b) and a song sequence (bottom / c). Song syllables are marked by letters A-G while call notes are marked by C1, C2, etc. The duration of each sound is listed in milliseconds. Jin et al. 2) A probabilistic map of notes in a bird’s song sequence. The pink oval represents the start note while blue ovals represent notes that the bird may end on.

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