Modeling phonotaxis in female Gryllus bimaculatus with artificial neural networks
© Meckenhäuser et al; licensee BioMed Central Ltd. 2011
Published: 18 July 2011
Here, we present a feed-forward artificial neural network that quantitatively predicts the attractiveness of mating songs. Our approach was motivated by recent work from Wittmann et al.  who presented a network that analyzes and evaluates courtship songs of grasshoppers (Chorthippus biguttulus).We studied networks consisting of twelve neurons in the input layer, each one representing a feature of a given song, a variable number of n = 1, … , 15 neurons in the hidden layer and one output neuron that represents the phonotaxis. The neurons from one layer to the next are all-to-all connected via synaptic weights. The weights are trained with 160 artificial courtship songs for which the phonotaxis had already been determined in experiments. For training our networks, we used the backpropagation algorithm.We show that the mean squared error computed from a test set of 40 courtship songs is minimal for artificial neural networks with n = 3 hidden neurons. To estimate the predictive power of 3-hidden-neurons networks, we analyze the correlation between the model’s phonotaxis and the female’s phonotaxis. Figure 1B. shows phonotaxis values predicted by 3-hidden-neurons networks versus the values measured in experiments. Each color represents one fixed network. The best performing network yielded a mean squared error of 0.05. Thus, our model can be used for a quantitative prediction of the attractiveness of untested courtship songs and so it complements experimental testing of female phonotaxis in the laboratory.
This work is funded by the German Research Council (DFG) within the Collaborative Research Center Theoretical Biology (SFB 618).
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