Toward a computational theory for the sensory world of bat biosonar

A sensory map of the world is a key component of any cognitive system. For cognitive systems navigating the physical world, this map is typically assumed to be a representation of the three-dimensional geometries in the environment. For vision-based maps, this is not an overly difficult goal to accomplish, since images have already two dimensions and hence only depth has to be inferred from additional clues. For bats using biosonar to navigate complex natural environments, such as dense vegetation, reconstruction of scatterer geometry is likely an ill-posed problem under the constraints of the biosonar systems (e.g., on beamwidth and spatial sampling). In these cases, biosonar echoes are “clutter,” i.e., signals that must be regarded as unpredictable due to lack of knowledge. Deep learning offers an opportunity to explore the information content of these echoes and hence understand the sensory map of bat biosonar on small and large scales. In addition, the biosonar systems of many bat species that live in dense vegetation are highly active senses, where time-variant signal transformations of the emitted pulses and the returning echoes are controlled in a perception-action loop. Again, deep learning can be used to understand the information that is encoded in this loop.