# Building a Complete, Predictive, Data-Driven Model of Action Selection During Olfactory Navigation

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA SANTA BARBARA · 2023 · $388,750

## Abstract

Abstract
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).

## Key facts

- **NIH application ID:** 10692730
- **Project number:** 5R01NS113048-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA SANTA BARBARA
- **Principal Investigator:** MATTHIEU R. P. J. C. G. LOUIS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $388,750
- **Award type:** 5
- **Project period:** 2019-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10692730

## Citation

> US National Institutes of Health, RePORTER application 10692730, Building a Complete, Predictive, Data-Driven Model of Action Selection During Olfactory Navigation (5R01NS113048-05). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10692730. Licensed CC0.

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