Bats can use the characteristics of other bats' voices to recognize each other. This is one of the results of our recent collaborative study with Y. Yovel, M. Melcon, A. Denziger and H.-U- Schnitzler from the University of Tübingen. The study, published June 5 in the open-access journal PLoS Computational Biology, explains how bats use echolocation for more than just spatial knowledge.

 

We first tested the ability of four greater mouse-eared bats to distinguish between the echolocation calls of other bats. After observing that the bats learned to discriminate the voices of other bats, we developed a computer model based on machine learning techniques that reproduces the recognition behaviour of the bats. Analysis of our model suggests that the spectral energy distribution in the signals contains individual-specific information that allows one bat to recognize another.

Animals must recognize each other in order to engage in social behaviour. Vocal communication signals are helpful for recognizing individuals, especially in nocturnal organisms such as bats. Little is known about how bats perform strenuous social tasks, such as remaining in a group when flying at high speeds in darkness, or avoiding interference between echolocation calls. The finding that bats can recognize other bats within their own species based on their echolocation calls may therefore have some significant implications.

[ Link to the original paper, press echo in BBC Worlwide, ORF News, Discover Magazine. ]

In a previous study, we investigated whether machine learning algorithms can be used to extract biologically plausible features from complex ultrasound echoes created by ensonifying various plants with bat-like chirps. The resulting ultrasonic features turned out to be surprisingly simple: a few combinations of time-frequency channels were sufficient to classify plant echoes with high accuracy.

[ Link to the original paper, press echo in Science Now, Spiegel Online and Innovationsreport. ]

The reasons why plant classification is possible at all were investigated in a third study where we examined the statistics of natural vegetation echoes. Vegetation echoes constitute a major part of the sensory world of more than 800 species of echolocating bats and play an important role in several of their daily tasks. Our statistical analysis is based on a large collection of plant echoes acquired by a biomimetic sonar system. We explore the relation between the physical world (the structure of the plant) and the characteristics of its echo. Finally, we complete the story by analyzing the effect of the sensory processing of both the echolocation and the auditory systems on the echoes and interpret them in the light of information maximization. The echoes of all different plant species we examined share a surprisingly robust pattern that was also reproduced by a simple Poisson model of the spatial reflector arrangement. The fine differences observed between the echoes of different plant species can be explained by the spatial characteristics of the plants. The bat's emitted signal enhances the most informative spatial frequency range where the species-specific information is large. The auditory system filtering affects the echoes in a similar way, thus enhancing the most informative spatial frequency range even more. These findings suggest how the bat's sensory system could have evolved to deal with complex natural echoes.

[ Link to original paper ]