Discriminating Gourmets, Lovers, and Enophiles? Neural Nets Tell All About Locusts, Toads, and Roaches

  • Wayne M. Getz
  • William C. Lemon
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


Here we consider the issue of choice and how neural systems can be used to investigate the processes of discrimination, as well as the evolution of different kinds of choice-related behavior in animals. We develop these ideas in the context of three studies, among others. The first study is on the evolution of specialization in animals using locust feeding behavior as the leitmotif, where decision making in individuals is modeled by a 3-layer-perceptron. In this study the fitness of individuals depends on their response to signals from plants and the density of individuals using those plants [1]. The second is a study that investigates the evolution of species recognition in sympatric taxa using female mate choice in frogs as the leitmotif [2]. Here individuals are modeled by Elman nets (3-layered perceptrons with feedback) and their fitness is determined by their ability to discriminate conspecifics from heterospecifics. The third is a study of the response characteristics of a recurrent Hopfield-type neural network to input that represents olfactory stimuli. The connectivity of this net reflects the basic architectural features of the neuron in the insect antennal lobe, as typified by cockroaches or bees [3].


Neural Network Model Plant Type Projection Neuron Synaptic Weight Mushroom Body 
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  1. [1]
    Holmgren NMA, Getz WM. Evolution of host plant selection in insects under perceptual constraints: a simulation study. Evol Ecol Res 2000; 2:1–25Google Scholar
  2. [2]
    Ryan MJ, Getz WM. Signal decoding and receiver evolution: an analysis using a neural network. In preparationGoogle Scholar
  3. [3]
    Getz WM, Lutz A. A neural network model of general olfactory coding in the insect antennal lobe. Chem Senses 1999; 24:351–372CrossRefGoogle Scholar
  4. [4]
    Bernays EA, Wcislo WT Sensory capabilities, information-processing, and resource specialization. Q. Rev. Biol. 1994: 69:(2) 187–204CrossRefGoogle Scholar
  5. [5]
    Bernays EA. The value of being a resource specialist: Behavioral support for a neural hypothesis. American Naturalist 1998: 151:(5) 451–464CrossRefGoogle Scholar
  6. [6]
    Nolfi S, Parisi D. Learning to adapt to changing environments in evolving neural networks. Adaptive Behavior 1996; 5:75–98CrossRefGoogle Scholar
  7. [7]
    Nolfi S, Parisi D, Elman JL. Learning and evolution in neural networks. Adaptive Behavior 1994; 3:5–28CrossRefGoogle Scholar
  8. [8]
    Ghirlanda S, Enquist M. Artificial neural networks as models of stimulus control. Anim Behav 1998; 56:1383–1389CrossRefGoogle Scholar
  9. [9]
    Bolhuis JJ. The development of animal behavior: from Lorenz to neural nets. Naturwissenschaften 1999; 86:101–111CrossRefGoogle Scholar
  10. [10]
    Leow WK. Computational studies of exploration by smell. Adaptive Behavior 1998; 6:411–434CrossRefGoogle Scholar
  11. [11]
    Toquenaga Y, Kajitani I, Hoshino T. Egrets of a feather flock together. Artificial Life 1994; 1:391–411CrossRefGoogle Scholar
  12. [12]
    Schmajuk NA, Zanutto BS. Escape, avoidance and imitation: a neural network approach. Adaptive Behavior 1997; 6:63–129CrossRefGoogle Scholar
  13. [13]
    Holmgren NA, Enquist M. Dynamics of mimicry evolution. Biol J Linn Soc 1999; 66: 145–158CrossRefGoogle Scholar
  14. [14]
    Phelps SM, Ryan MJ. Neural networks predict response biases of female tungara frogs. Proc Royal Soc Lond B 1998; 265:279–285CrossRefGoogle Scholar
  15. [15]
    Arak A, Enquist M. Conflict, receiver bias and the evolution of signal form. Phil Trans Royal Soc Lond B 1995; 349:337–344CrossRefGoogle Scholar
  16. [16]
    Krakauer DC, Johnstone RA. The evolution of exploitation and honesty in animal communication: a model using artificial neural networks. Phil Trans Royal Soc Lond B 1995; 348:355–361CrossRefGoogle Scholar
  17. [17]
    Enquist M, Arak A. Symmetry, beauty and evolution. Nature 1994; 372:169–172CrossRefGoogle Scholar
  18. [18]
    Johnstone RA. Female preference for symmetrical males is a by-product of selection for mate recognition. Nature 1994; 372:172–175CrossRefGoogle Scholar
  19. [19]
    Enquist M, Johnstone RA. Generalization and the evolution of symmetry preferences. Proc Royal Soc Lond B 1997; 264:1345–1348CrossRefGoogle Scholar
  20. [20]
    Dawkins MS, Guilford T. An exaggerated preference for simple neural network models of signal evolution? Proc Royal Soc Lond B 1995; 261:357–360CrossRefGoogle Scholar
  21. [21]
    Sarikaya M, Wang W, Ogmen H. Neural network model of on-off units in the fly visual system: simulations of dynamic behavior. Biological Cybernetics 1998; 78:399–412MATHCrossRefGoogle Scholar
  22. [22]
    Morse TM, Ferree TC, Lockery SR. Robust spatial navigation in a robot inspired by chemotaxis in Caenorhabditis elegans. Adaptive Behavior 1998; 6:393–410CrossRefGoogle Scholar
  23. [23]
    Snyder MR. A functionally equivalent artificial neural network model of the prey orientation behavior of waterstriders (Gerridae). Ethology 1998; 104:285–297CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Wayne M. Getz
    • 1
  • William C. Lemon
    • 1
  1. 1.Division of Insect BiologyUniversity of California BerkeleyUSA

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