Journal of Computational Neuroscience

, Volume 35, Issue 1, pp 75–85 | Cite as

Computational principles underlying the recognition of acoustic signals in insects

  • Jan Clemens
  • R. Matthias Hennig


Many animals produce pulse-like signals during acoustic communication. These signals exhibit structure on two time scales: they consist of trains of pulses that are often broadcast in packets—so called chirps. Temporal parameters of the pulse and of the chirp are decisive for female preference. Despite these signals being produced by animals from many different taxa (e.g. frogs, grasshoppers, crickets, bushcrickets, flies), a general framework for their evaluation is still lacking. We propose such a framework, based on a simple and physiologically plausible model. The model consists of feature detectors, whose time-varying output is averaged over the signal and then linearly combined to yield the behavioral preference. We fitted this model to large data sets collected in two species of crickets and found that Gabor filters—known from visual and auditory physiology—explain the preference functions in these two species very well. We further explored the properties of Gabor filters and found a systematic relationship between parameters of the filters and the shape of preference functions. Although these Gabor filters were relatively short, they were also able to explain aspects of the preference for signal parameters on the longer time scale due to the integration step in our model. Our framework explains a wide range of phenomena associated with female preference for a widespread class of signals in an intuitive and physiologically plausible fashion. This approach thus constitutes a valuable tool to understand the functioning and evolution of communication systems in many species.


Perceptual decision making Insect Song Linear-nonlinear model Gabor filter 



We thank Klaus-Gerhardt Heller for valuable discussions.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Behavioral Physiology Group, Department of BiologyHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Bernstein Center for Computational Neuroscience BerlinBerlinGermany
  3. 3.Princeton Neuroscience InstitutePrinceton UniversityPrincetonUSA

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