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

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...

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Autores principales: Clemens, Jan, Schöneich, Stefan, Kostarakos, Konstantinos, Hennig, R Matthias, Hedwig, Berthold
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635984/
https://www.ncbi.nlm.nih.gov/pubmed/34761750
http://dx.doi.org/10.7554/eLife.61475
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author Clemens, Jan
Schöneich, Stefan
Kostarakos, Konstantinos
Hennig, R Matthias
Hedwig, Berthold
author_facet Clemens, Jan
Schöneich, Stefan
Kostarakos, Konstantinos
Hennig, R Matthias
Hedwig, Berthold
author_sort Clemens, Jan
collection PubMed
description 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 arises 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.
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spelling pubmed-86359842021-12-03 A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets Clemens, Jan Schöneich, Stefan Kostarakos, Konstantinos Hennig, R Matthias Hedwig, Berthold eLife Evolutionary Biology 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 arises 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. eLife Sciences Publications, Ltd 2021-11-11 /pmc/articles/PMC8635984/ /pubmed/34761750 http://dx.doi.org/10.7554/eLife.61475 Text en © 2021, Clemens et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Evolutionary Biology
Clemens, Jan
Schöneich, Stefan
Kostarakos, Konstantinos
Hennig, R Matthias
Hedwig, Berthold
A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
title A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
title_full A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
title_fullStr A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
title_full_unstemmed A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
title_short A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
title_sort small, computationally flexible network produces the phenotypic diversity of song recognition in crickets
topic Evolutionary Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635984/
https://www.ncbi.nlm.nih.gov/pubmed/34761750
http://dx.doi.org/10.7554/eLife.61475
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