Loading...
Dynamics of Sensory Integration of Olfactory and Mechanical Stimuli Within the Response Patterns of Moth Antennal Lobe Neurons
Tuckman, Harrison
Tuckman, Harrison
Abstract
Odors emanating from a biologically relevant source are rapidly embedded within a windy, turbulent medium that folds and spins filaments into fragmented strands of varying sizes. Environmental odor plumes therefore exhibit complex spatiotemporal dynamics, and rarely yield an easily discernible concentration gradient marking an unambiguous trail to an odor source. Thus, sensory integration of chemical input, encoding odor identity or concentration, and mechanosensory input, encoding wind speed, is a critical task that animals face in resolving the complex dynamics of odor plumes and tracking an odor source. In insects, who employ olfactory navigation as their primary means of foraging for food and finding mates, the antennal lobe (AL) is the first brain structure that processes sensory odor information. Although the importance of chemosensory and mechanosensory integration is widely recognized, the AL itself has traditionally been viewed purely from the perspective of odor encoding, with little attention given to its role as a bimodal integrator. In this work, we seek to establish the AL as an ideal model for studying sensory integration – it boasts well-understood architecture, well-studied olfactory responses, and easily measurable cells. Experimental studies suggest that mechanosensory responses are transient and temporally precise, while olfactory responses are long-lasting but lack temporal precision. Within this work, we develop a computational model of the AL that captures these odor response dynamics, and then examine the dynamics of our model with the inclusion of mechanosensory input. Through use of this model, we pinpoint dynamical mechanisms potentially underlying the bimodal AL responses revealed in experimental studies. Finally, we propose a novel hypothesis about the role of mechanosensory input in sculpting AL dynamics and the implications for biological odor tracking.
Description
Date
2020-05-01
Journal Title
Journal ISSN
Volume Title
Publisher
Collections
Download Dataset
Rights Holder
Usage License
Embargo
Research Projects
Organizational Units
Journal Issue
Keywords
Citation
Department
Mathematics