| Summary: | Horseshoe bats (family Rhinolophidae) are among the bat species that dynamically deform
their reception baffles (pinnae) and emission baffles (noseleaves) during signal reception and
emissions, respectively. These dynamics are a focus of prior studies that demonstrated that
these effects could introduce time-variance within emitted and received signals. Recent lab based
experiments with biomimetic hardware have shown that these dynamics can also inject
time-variant signatures into echoes from simple targets. However, complex foliage echoes,
which comprise a large portion of the received echoes and contain useful information for
these bats, have not been studied in prior research. We used a biomimetic sonarhead which
replicated these dynamics, to collect a large dataset of foliage echoes (>55,000). To generate
a neuromorphic representation of echoes that was representative of the neural spikes in bat
brains, we developed an auditory processing model based on Horseshoe bat physiological
data. Then, machine learning classifiers were employed to classify these spike representations
of echoes into distinct groups, based on the presence or absence of dynamics' effects.
Our results showed that classification with up to 80% accuracy was possible, indicating the
presence of these effects in foliage echoes, and their persistence through the auditory processing.
These results suggest that these dynamics' effects might be present in bat brains, and
therefore have the potential to inform behavioral decisions. Our results also indicated that
potential benefits from these effects might be location specific, as our classifier was more
effective in classifying echoes from the same physical location, compared to a dataset with
significant variation in recording locations. This result suggested that advantages of these
effects may be limited to the context of particular surroundings if the bat brain similarly
fails to generalize over variation in locations. === Master of Science === Horseshoe bats (family Rhinolophidae) are an echolocating bat species, i.e., they emit sound
waves and use the corresponding echoes received from the environment to gather information
for navigation. This species of bats demonstrate the behavior of deforming their emitter
(noseleaf), and ears (pinna), while emitting or receiving echolocation signals. Horseshoe
bats are adept at navigating in the dark through dense foliage. Their impressive navigational
abilities are of interest to researchers, as their biology can inspire solutions for autonomous
drone navigation in foliage and underwater. Prior research, through numerical studies and
experimental reproductions, has found that these deformations can introduce time-dependent
changes in the emitted and received signals. Furthermore, recent research using a biomimetic
robot has found that echoes received from simple shapes, such as cube and sphere, also
contain time-dependent changes. However, prior studies have not used foliage echoes in
their analysis, which are more complex, since they include a large number of randomly
distributed targets (leaves). Foliage echoes also constitute a large share of echoes from the
bats' habitats, hence an understanding of the effects of the dynamic deformations on these
foliage echoes is of interest. Since echolocation signals exist within bat brains as neural spikes,
it is also important to understand if these dynamic effects can be identified within such signal
representations, as that would indicate that these effects are available to the bats' brains. In
this study, a biomimetic robot that mimicked the dynamic pinna and noseleaf deformation
was used to collect a large dataset (>55,000) of echoes from foliage. A signal processing model
that mimicked the auditory processing of these bats and generated simulated spike responses
was also developed. Supervised machine learning was used to classify these simulated spike
responses into two groups based on the presence or absence of these dynamics' effects. The
success of the machine learning classifiers of up to 80% accuracy suggested that the dynamic
effects exist within foliage echoes and also spike-based representations. The machine learning
classifier was more accurate when classifying echoes from a small confined area, as compared
to echoes distributed over a larger area with varying foliage. This result suggests that any
potential benefits from these effects might be location-specific if the bat brain similarly fails
to generalize over the variation in echoes from different locations.
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