CAREER: Navigation in Naturalistic and Changing Environments: Perception, Learning and Memory

California State University-Fresno Foundation, Fresno CA

Investigators

Abstract

This Faculty Early-Career Development (CAREER) award supports an integrated research and education project aimed at determining how insects navigate in complex environments. The ultimate goal of this project is to determine how animals navigate in dynamic environments to advance our understanding of the perceptual, learning, and memory mechanisms underlying navigation. The outcomes of this research provide an understanding of dynamic control systems, reveal how the nervous system has evolved to handle unpredictability, and drive the development of novel navigational algorithms and biological sensors. This project is designed to provide learning and research opportunities to expand interdisciplinary education for underrepresented groups in the STEM majors. The investigator integrates this research into curriculum at multiple levels: (i) an introductory module that engages first-year biology students to use models and simulations to understand how animals move through the world; (ii) the development of a summer research program that recruits undergraduate students; (iii) the establishment of collaboration with high-school biology teachers to help establish the interdisciplinary approach to biology education before students enter university. Insect navigation studies have a long history, but the mechanisms of control in changing naturalistic environments remain relatively unexamined. An integrative approach is used to elucidate how perception of the environment is computed and stored in the nervous system and acted upon by an organism. There are three major aims in the proposed research: (i) identify the local and global visual features that ants extract from naturalistic panoramic scenes; (ii) determine how the viewing behavior of ants comes to be directed towards the local and global cues used for route guidance; (iii) define the role of memory in stabilizing routes in the face of change. The proposed research brings the unpredictable nature of the field into the lab by employing virtual naturalistic environments in which complexity and instability can be introduced in a systematic fashion. For example, the investigator can add, remove, or displace the visual features within a scene in a closed-loop fashion simulating natural occurring changes in lighting conditions, wind displacement, and terrain geometry. This research is of general interest to visual and behavioral neuroscientists and to computer scientists working with autonomous robots. Visually dependent tasks that are broadly similar across a wide variety of animals with well-developed visual systems tend to have areas of convergence in their generating algorithms (e.g. path integration, visually guided navigation), although the specific neural implementation may differ between species. Insect navigational behavior provides an ideal platform to develop precise control models to describe and simulate cellular events, neural circuits, and complex behavior.