Biomimetic Detection of Dynamic Signatures in Foliage Echoes
MetadataShow full item record
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.
General Audience Abstract
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.
- Masters Theses