A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
- European Neuroscience Institute, Germany
- Friedrich-Schiller-University, Germany
- University of Graz, Austria
- Humboldt-Universität Berlin, Germany
- University of Cambridge, United Kingdom
Abstract
How neural networks evolved to generate the diversity of species-specific communication signals is unknown. For receivers of the signals one hypothesis is that novel recognition phenotypes arise from parameter variation in computationally flexible feature detection networks. We test this hypothesis in crickets, where males generate and females recognize the mating songs with a species-specific pulse pattern, by investigating whether the song recognition network in the cricket brain has the computational flexibility to recognize different temporal features. Using electrophysiological recordings from the network that recognizes crucial properties of the pulse pattern on the short timescale in the cricket Gryllus bimaculatus, we built a computational model that reproduces the neuronal and behavioral tuning of that species. An analysis of the model's parameter space reveals that the network can provide all recognition phenotypes for pulse duration and pause known in crickets and even other insects. Phenotypic diversity in the model is consistent with known preference types in crickets and other insects, and arise from computations that likely evolved to increase energy efficiency and robustness of pattern recognition. The model's parameter to phenotype mapping is degenerate-different network parameters can create similar changes in the phenotype-which likely supports evolutionary plasticity. Our study suggests that computationally flexible networks underlie the diverse pattern recognition phenotypes and we reveal network properties that constrain and support behavioral diversity.
Data availability
We are in the process of uploading the previously published data (which had not been deposited before) used for fitting the model tohttps://data.goettingen-research-online.de/dataverse/cricketnetThe source code required for running the model was deposited athttps://github.com/janclemenslab/cricketnet
Article and author information
Author details
Funding
Biotechnology and Biological Sciences Research Council (BB/J01835X/1)
- Konstantin Kostarakos
- Berthold Hedwig
Royal Society (Newton International Fellowship)
- Konstantin Kostarakos
Leibniz-Gemeinschaft (SAW 2012-MfN-3)
- R Matthias Hennig
Deutsche Forschungsgemeinschaft (HE 2812/4-1)
- R Matthias Hennig
Deutsche Forschungsgemeinschaft (HE 2812/5-1)
- R Matthias Hennig
Deutsche Forschungsgemeinschaft (CL 596/1-1)
- Jan Clemens
Deutsche Forschungsgemeinschaft (CL 596/2-1)
- Jan Clemens
Deutsche Forschungsgemeinschaft (SCHO 1822/3-1)
- Stefan Schöneich
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Ronald L Calabrese, Emory University, United States
Version history
- Preprint posted: July 27, 2020 (view preprint)
- Received: July 27, 2020
- Accepted: November 3, 2021
- Accepted Manuscript published: November 11, 2021 (version 1)
- Version of Record published: December 1, 2021 (version 2)
Copyright
© 2021, Clemens et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
eLife 10:e61475.
https://doi.org/10.7554/eLife.61475
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