ABSTRACT
Many animals, humans included, rely on acoustic vocalizations for communication. The complexity of non-human vocal communication has been under debate one of the main open questions being: What could be the function of multi-syllabic vocal sequences? We address these questions by analyzing fruit-bat vocal communication. We use neural networks to encode the vocalizations, and statistical models to examine the information conveyed by sequences of vocalizations. We show that fruit-bat vocal sequences potentially convey more contextual information than individual syllables, but that the order of the syllables within the sequence is unimportant for context. Specifically, sequences are composed of slightly modified syllables, thus increasing the probability of context-specificity. We note that future behavioral, e.g., playback experiments are needed in order to validate the biological relevance of our statistical results. We hypothesize that such sequences might have served as pre-syntax precursors in the evolution of animal communication.
ABSTRACT
During hundreds of millions of years of evolution, insects have evolved some of the most efficient and robust sensing organs, often far more sensitive than their man-made equivalents. In this study, we demonstrate a hybrid bio-technological approach, integrating a locust tympanic ear with a robotic platform. Using an Ear-on-a-Chip method, we manage to create a long-lasting miniature sensory device that operates as part of a bio-hybrid robot. The neural signals recorded from the ear in response to sound pulses, are processed and used to control the robot's motion. This work is a proof of concept, demonstrating the use of biological ears for robotic sensing and control.
