Biomimetic detection of dynamic signatures in foliage echoes.

Certain bat species (family Rhinolophidae) dynamically deform their emission baffles (noseleaves) and reception baffles (pinnae) during echolocation. Prior research using numerical models, laboratory characterizations, and experiments with simple targets have suggested that this dynamics may manifest itself in time-variant echo signatures. Since the pronounced random nature of echoes from natural targets such as foliage has not been reflected in these experiments, we have collected a large number (>55,000) of foliage echoes outdoors with a sonar head that mimics the dynamic periphery in bats. The echo data was processed with a custom auditory processing model to create spike-based echo representations. Deep-learning classifiers were able to estimate the dynamic state of the periphery, i.e., static or dynamic, based on single echoes with accuracies of up to 80%. This suggests that the effects of the peripheral dynamics are present in the bat brains and could hence be used by the animals. The best classification performances were obtained for data that was obtained within a spatially confined area. Hence, if the bat brains suffer from the same generalization issues, they would have to have a way to adapt their neural echo processing to such local fluctuations to exploit the dynamic effects successfully.