Skip to main content
Proceedings of the Royal Society B: Biological Sciences logoLink to Proceedings of the Royal Society B: Biological Sciences
. 1998 Feb 22;265(1393):279–285. doi: 10.1098/rspb.1998.0293

Neural networks predict response biases of female túngara frogs.

S M Phelps 1, M J Ryan 1
PMCID: PMC1688888  PMID: 9523430

Abstract

Artificial neural networks have become useful tools for probing the origins of perceptual biases in the absence of explicit information on underlying neuronal substrates. Preceding studies have shown that neural networks selected to recognize or discriminate simple patterns may possess emergent biases toward pattern size of symmetry--preferences often exhibited by real females--and have investigated how these biases shape signal evolution. We asked whether simple neural networks could evolve to respond to an actual mate recognition signal, the call of the túngara frog, Physalaemus pustulosus. We found that not only were networks capable of recognizing the call of the túngara frog, but that they made remarkably accurate quantitative predictions about how well females generalized to many novel calls, and that these predictions were stable over several architectures. The data suggest that the degree to which P. pustulosus females respond to a call may often be an incidental by-product of a sensory system selected simply for species recognition.

Full Text

The Full Text of this article is available as a PDF (358.9 KB).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. BLAIR W. F. ISOLATING MECHANISMS AND INTERSPECIES INTERACTIONS IN ANURAN AMPHIBIANS. Q Rev Biol. 1964 Dec;39:334–344. doi: 10.1086/404324. [DOI] [PubMed] [Google Scholar]
  2. Basolo A. L. Female preference predates the evolution of the sword in swordtail fish. Science. 1990 Nov 9;250(4982):808–810. doi: 10.1126/science.250.4982.808. [DOI] [PubMed] [Google Scholar]
  3. Dawkins M. S., Guilford T. An exaggerated preference for simple neural network models of signal evolution? Proc Biol Sci. 1995 Sep 22;261(1362):357–360. doi: 10.1098/rspb.1995.0159. [DOI] [PubMed] [Google Scholar]
  4. Enquist M., Arak A. Selection of exaggerated male traits by female aesthetic senses. Nature. 1993 Feb 4;361(6411):446–448. doi: 10.1038/361446a0. [DOI] [PubMed] [Google Scholar]
  5. Enquist M., Arak A. Symmetry, beauty and evolution. Nature. 1994 Nov 10;372(6502):169–172. doi: 10.1038/372169a0. [DOI] [PubMed] [Google Scholar]
  6. Johnstone R. A. Female preference for symmetrical males as a by-product of selection for mate recognition. Nature. 1994 Nov 10;372(6502):172–175. doi: 10.1038/372172a0. [DOI] [PubMed] [Google Scholar]
  7. Kirkpatrick M., Rosenthal G. G. Animal behaviour. Symmetry without fear. Nature. 1994 Nov 10;372(6502):134–135. doi: 10.1038/372134a0. [DOI] [PubMed] [Google Scholar]
  8. Ryan M. J., Fox J. H., Wilczynski W., Rand A. S. Sexual selection for sensory exploitation in the frog Physalaemus pustulosus. Nature. 1990 Jan 4;343(6253):66–67. doi: 10.1038/343066a0. [DOI] [PubMed] [Google Scholar]
  9. Ryan M. J. Neuroanatomy influences speciation rates among anurans. Proc Natl Acad Sci U S A. 1986 Mar;83(5):1379–1382. doi: 10.1073/pnas.83.5.1379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ryan M. J., Rand A. S. Female responses to ancestral advertisement calls in tungara frogs. Science. 1995 Jul 21;269(5222):390–392. doi: 10.1126/science.269.5222.390. [DOI] [PubMed] [Google Scholar]
  11. Tank D. W., Hopfield J. J. Neural computation by concentrating information in time. Proc Natl Acad Sci U S A. 1987 Apr;84(7):1896–1900. doi: 10.1073/pnas.84.7.1896. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Proceedings of the Royal Society B: Biological Sciences are provided here courtesy of The Royal Society

RESOURCES