A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets

  1. Jan Clemens  Is a corresponding author
  2. Stefan Schöneich
  3. Konstantin Kostarakos
  4. R Matthias Hennig
  5. Berthold Hedwig
  1. European Neuroscience Institute, Germany
  2. Friedrich-Schiller-University, Germany
  3. University of Graz, Austria
  4. Humboldt-Universität Berlin, Germany
  5. 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

  1. Jan Clemens

    Neural Computation and Behavior group, European Neuroscience Institute, Göttingen, Germany
    For correspondence
    clemensjan@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4200-8097
  2. Stefan Schöneich

    Institute for Zoology and Evolutionary Research, Friedrich-Schiller-University, Jena, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4503-5111
  3. Konstantin Kostarakos

    Institute of Biology, University of Graz, Graz, Austria
    Competing interests
    The authors declare that no competing interests exist.
  4. R Matthias Hennig

    Humboldt-Universität Berlin, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Berthold Hedwig

    Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

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.

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.

Metrics

  • 912
    views
  • 135
    downloads
  • 12
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jan Clemens
  2. Stefan Schöneich
  3. Konstantin Kostarakos
  4. R Matthias Hennig
  5. Berthold Hedwig
(2021)
A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
eLife 10:e61475.
https://doi.org/10.7554/eLife.61475

Share this article

https://doi.org/10.7554/eLife.61475

Further reading

    1. Evolutionary Biology
    2. Genetics and Genomics
    Julie N Chuong, Nadav Ben Nun ... David Gresham
    Research Article

    Copy number variants (CNVs) are an important source of genetic variation underlying rapid adaptation and genome evolution. Whereas point mutation rates vary with genomic location and local DNA features, the role of genome architecture in the formation and evolutionary dynamics of CNVs is poorly understood. Previously, we found the GAP1 gene in Saccharomyces cerevisiae undergoes frequent amplification and selection in glutamine-limitation. The gene is flanked by two long terminal repeats (LTRs) and proximate to an origin of DNA replication (autonomously replicating sequence, ARS), which likely promote rapid GAP1 CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we evolved engineered strains lacking either the adjacent LTRs, ARS, or all elements in glutamine-limited chemostats. Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. Removal of local DNA elements significantly impacts the fitness effect of GAP1 CNVs and the rate of adaptation. In 177 CNV lineages, across all four strains, between 26% and 80% of all GAP1 CNVs are mediated by Origin Dependent Inverted Repeat Amplification (ODIRA) which results from template switching between the leading and lagging strand during DNA synthesis. In the absence of the local ARS, distal ones mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination can mediate gene amplification following de novo retrotransposon events. Our study reveals that template switching during DNA replication is a prevalent source of adaptive CNVs.

    1. Evolutionary Biology
    Mattias Siljestam, Claus Rueffler
    Research Article Updated

    The majority of highly polymorphic genes are related to immune functions and with over 100 alleles within a population, genes of the major histocompatibility complex (MHC) are the most polymorphic loci in vertebrates. How such extraordinary polymorphism arose and is maintained is controversial. One possibility is heterozygote advantage (HA), which can in principle maintain any number of alleles, but biologically explicit models based on this mechanism have so far failed to reliably predict the coexistence of significantly more than 10 alleles. We here present an eco-evolutionary model showing that evolution can result in the emergence and maintenance of more than 100 alleles under HA if the following two assumptions are fulfilled: first, pathogens are lethal in the absence of an appropriate immune defence; second, the effect of pathogens depends on host condition, with hosts in poorer condition being affected more strongly. Thus, our results show that HA can be a more potent force in explaining the extraordinary polymorphism found at MHC loci than currently recognised.