Darwin first proposed that animals might navigate using a method of directional odometry to track their place in
the world. In modern behavioral neuroscience, this algorithm is known as path integration. Since Darwin's
speculation, researchers presented evidence suggesting that a plethora of species, including ants, insects, and
mammals employ this mechanism in finding their home or other desirable locations such as food sources. Even
though researchers have studied path integration for decades, the specific neural mechanisms underlying these
computations are largely unknown, likely due to the complexity of mammalian brains that have been the focus
of studies. This deficiency has hindered advances in the basic science of executive control and led to a lack of
a detailed mechanistic description of fluidity, persistence, and working memory which are compromised in many
brain disorders such as schizophrenia and Alzheimer’s disease. The fruit fly, Drosophila melanogaster, offers
many advantages over mammals in investigating these mechanisms, such as rapid experimental timelines, the
ability to track and quantify the behavior of large numbers of individuals in controlled laboratory chambers, and
the tractability of the nervous system. Besides, due to the work of countless researchers, many individual neuron
classes can be targeted with genetic tools in behaving flies. Like most animals that are not central place foragers,
Drosophila is not renowned for its path integration ability. However, recent research demonstrates that it may be
possible to uncover the neural substrates of path integration in flies. After finding the food, flies perform local
searches in which they venture away from and revisit the location where they had encountered the food. A variety
of evidence suggests that the animals’ ability to navigate back to the food during these local searches involves
path integration. In my recent study, I confined flies to a circular channel and provided them with fictive food
through optogenetic stimulation of sugar receptors. Under this paradigm, flies robustly elicited local search by
visiting the fictive food location multiple times via path integration in the absence of external cues. I will continue
my work, by specifically focusing on the involvement of the Central Complex (CX), a set of unpaired nuclei in the
core of the insect brain, which is thought to have a prominent role in navigation. I plan to use agent-based
modeling, as well as pattern recognition algorithm on flies’ search trajectories, along with genetic manipulation
of the fly’s brain to test the necessity/roles of specific cells in various aspects of path integration behavior. I will
construct a testable model for the navigation circuit describing the roles of specific cells. During the R00 period,
I will take a hybrid approach to develop a testable ethologically motivated model for path integration based on
hypotheses in my first aims. My project will also serve as a basis to address one of the important misconceptions
in the engineering community regarding the optimality of biological systems which not only hindered progress in
modeling efforts but also resulted in many unimplementable bio-inspired engineering designs.