I would like to thank Dr. Rolf Müller for giving me the opportunity to pursue this research. I appreciate immensely his trust and guidance throughout my time working on this research.His leadership, approach to problem-solving, attention to detail, and seemingly limitless wide-ranging knowledge of science, are the reference points I will always aspire to reach in my career. I would also like to thank my committee members, Dr. Alexander Leonessa and Dr. Michael Roan, for their mentorship and technical advice. I wish to acknowledge Dr.Mohammad Omar Khyam and David Alexandre, who contributed to a significant part of this research. I'd also like to acknowledge Liujun Zhang, for his help in collecting the data for this project, and for his friendship. I'd also like to thank the other grad students in the lab
Many bat species live in densely vegetated habitats which pose challenges to their biosonar systems. Among these challenges, the problem of identifying prey in clutter has received the most attention. Here, the far less well-studied problem of landmark identification in forest environments has been investigated. To this end, a large data set of about 220 000 foliage echoes has been collected along different tracks located in forested area. The echoes were recorded using a biomimetic sonarhead with flexible noseleaf and pinnae modeled on the periphery of horseshoe bats. Low-dimensional representations of these foliage echoes were created with the encoder portion of a variational autoencoder deep neural network architecture. The feature vectors obtained in this manner were subjected to clustering to determine whether the echo recordings exhibited continuous variability or fell into discernible clusters. The data silhouettes indicate the presence of a small number of distinguishable clusters in the echo data. Furthermore, mapping the assigned cluster labels to the geographical coordinates of the respective echo recordings revealed that different tracks were characterized by a different “fingerprints” of echo classes. Hence, these fingerprints could be a hypothetical basis for biosonar-based navigation in forest environments.
The sophisticated biosonar systems of horseshoe bats have enabled these animals to navigate and pursue prey in complex environments. A conspicuous peripheral dynamics in which the animals' noseleaves and pinnae change during biosonar behaviors could play an important role in enabling these capabilities. It may be hypothesized that for the integration between peripheral dynamics and neural signal processing/estimation to be maximally effective, the periphery should be controlled by feedback from the output of the subsequent neural echo processing. In the way, the specifics of sensory information encoding in the periphery could be controlled based on the needs of the neural signal processing. As a first step towards such an integration in a biomimetic sonar head, a computational model for the inner ear and the auditory nerve's spike code has been integrated with a dynamic periphery that—like the computational models—mimics horseshoe bats. For each model stage, alternative versions with different levels of complexity have been implemented to test how module complexity and the values of the associated parameters affect the capacity of the echo representation to encode sensory information. These effects have been tested based on a large dataset of 220 000 echoes collected in natural forest environments.
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