To survive, living organisms must collect information about their environment and use it to select appropriate behaviors. However, information from the environment is often noisy, incomplete and ambiguous. Currently, no theory or model comprehensively explains how nervous systems solve the problem of navigation based on noisy information. Without such a theory, we cannot improve the ability of living systems or autonomous machines to make better decisions by processing the imperfect sensory information that is typically available to them. We propose to build a complete data-driven model of how nervous systems turn noisy sensory information into action selection during navigation. We have previously been able to decipher aspects of this process by studying the Drosophila melanogaster larva ? a small, transparent organism that is exceptionally good at navigating towards food odors despite having only 10,000 neurons. My lab has developed methods to rigorously quantify odor landscapes; measure how neurons represent these odors; automatically track larval movement; create virtual sensory realities for the larva; and change the real-time behavior of the larva on- demand with optogenetics. We have also recently mapped an entire pathway within the larval nervous system. Here, we will determine how and when noisy sensory information causes the larva to reorient (stop and turn) as it is navigating towards an attractive odor source (chemotaxis). Our objective is to uncover the neural mechanisms that accumulate, filter, and process noisy sensory evidence and use ambiguous information to make coherent perceptual decisions (action selection). By combining theory, experiments, and modeling, we will iteratively build a quantitative model which predicts the cellular and circuit-level computations transforming sensory (olfactory) signals into navigational decision-making (chemotaxis) that is robust to environmental disturbances (noise).

Public Health Relevance

We do not yet understand why animals behave the way they do, why the same sensory input (e.g., a smell or a sound) can elicit vastly different behaviors across individuals. To study how animals interpret sensory information and turn this information into decisions for survival, we examine a simple creature ?a fly larva? as it performs one of the most important survival tasks: finding a food source. By quantifying, modeling, and then controlling the nervous system of a larva while it converts information from an ambiguous world into accurate navigational decisions, we aim to reveal the inner workings of a simple yet remarkably powerful brain and build new theories of how our own brain works.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
1R01NS113048-01
Application #
9817551
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
David, Karen Kate
Project Start
2019-08-01
Project End
2024-07-31
Budget Start
2019-08-01
Budget End
2020-07-31
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of California Santa Barbara
Department
Miscellaneous
Type
Organized Research Units
DUNS #
094878394
City
Santa Barbara
State
CA
Country
United States
Zip Code
93106