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.