Rajat Mittal (Department of Mechanical Engineering)

Jung Hee Seo (Department of Mechanical Engineering)

Cynthia F. Moss (Psychological and Brain Sciences)

Susanne J. Sterbing-D’Angelo (Psychological and Brain Sciences)

Animals that rely on active sensing provide a powerful system to investigate the neural underpinnings of natural scene representation, as they produce the very signals that inform motor actions. Echolocating bats, for example, transmit sonar signals and process auditory information carried by returning echoes to guide behavioral decisions for spatial orientation. Bats compute the direction of objects from differences in echo intensity, spectrum, and timing at the two ears; while an object’s distance is measured from the time delay between sonar emission and echo return. Together, this acoustic information gives rise to a 3D representation of the world through sound, and measurements of sonar calls and echoes provide explicit data on the signals available to the bat for orienting in space.

In the present seed funding program, we propose to develop a first-of-its-kind computational simulation-enabled method for echo scene analysis of an echolocating bat, which is based on acoustic simulations (we refer to this method as “EchoSIM”). The proposed method integrates tightly with free-flight laboratory assays of bats and takes as input, variables such as the bat’s flight path, hear-ear anatomy, position and orientation as well as the sonar call wave form. The simulation results (3D echo scene and echo signal) together with the experimental measurements will provide a unique and powerful integrated dataset that enable unprecedented analysis of active sensing and adaptive flight behavior of bats in complex environments.
An Iterative Approach to Integrating Environmental Genomics into Biogeochemical Models