Signal decoding and receiver evolution. An analysis using an artificial neural network.

We use a connectionist model, a recurrent artificial neural network, to investigate the evolution of species recognition in sympatric taxa. We addressed three questions: (1) Does the accuracy of artificial neural networks in discriminating between conspecifics and other sympatric heterospecifics depend on whether the networks were trained only to recognize conspecifics, as opposed to being trained to discriminate between conspecifics and sympatric heterospecifics? (2) Do artificial neural networks weight most heavily those signal features that differ most between conspecifics and sympatric heterospecifics, or those features that vary less within conspecifics? (3) Does selection for species recognition generate sexual selection? We find that: (1) Neural networks trained only on self recognition do not classify species as accurately as networks trained to discriminate between conspecifics and heterospecifics. (2) Neural networks weight signal features in a manner suggesting that the total sound environment as opposed to the relative variation of signals within the species is more important in the evolution of recognition mechanisms. (3) Selection for species recognition generates substantial variation in the relative attractiveness of signals within the species and thus can result in sexual selection.