In a recent study published in the open-access journal PLoS Computational Biology, researchers from Germany have created a computer algorithm that is able to mimic the way that bat’s classify plants using echolocation. The research combined machine learning scientists with biologists who study bats and echolocation.

Bats send out ultrasonic pulses – sound that is of a frequency greater than the upper limit of human hearing – in order to detect plants; then they decipher the different echoes that return. Plants are not only a daily food source for the animals, but also navigational markers that aid in differentiating foraging sites. Since there are so many reflections from leaves and branches, the echoes that come back from plants are highly complex signals. It is largely believed that it is quite difficult for bats to classify plants or other elaborate objects and scientists were unable to completely understand how the classification was done.

The research group from Tübingen, Germany, including University of Tübingen researchers Yossi Yovel, Peter Stilz and Hans Ulrich-Schnitzler, and Matthias Franz from the Max Planck Institute of Biological Cybernetics, has been able to shed some light on the way in which bats classify plants. Contrary to popular belief, they show that the process is not that complicated or difficult.

The researchers emitted bat-like, frequency modulated ultrasonic pulses using a sonar system and recorded thousands of echoes from five species of live plants. A computer algorithm used the time-frequency data of the echoes as input and accurately classified the plants. The researchers were also able to discern which echo characteristics could possibly be best-understood by bats.

The study is particularly interesting, according to the researchers, because it resulted in a better understanding of bat echolocation and plant classification without entering the bat’s brain.

Plant Classification from Bat-Like Echolocation Signals
Yovel Y, Franz MO, Stilz P, Schnitzler H-U
PLoS Computational Biology (2008). 4(3):e1000032.
doi:10.1371/journal.pcbi.1000032
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Written by: Peter M Crosta