# Odor trail tracking: a new paradigm to unveil algorithms and neural circuits underlying active sensation and continuous decision making

> **NIH NIH RF1** · HARVARD UNIVERSITY · 2022 · $2,730,261

## Abstract

Summary
Animals actively sample sensory information, which they combine with prior knowledge to make
decisions in a sensorimotor feedback loop. Aspects of this complex loop are often studied in
isolation, using trial structures and in simplified conditions such as head-restrained animals in
virtual reality. Studying an ethologically relevant, natural behavior in the laboratory can offer
deeper insights about the behavioral strategies and their mechanistic neural implementation.
Odor trail tracking is one such behavior, observed in many terrestrial animals including mice,
and involves continuous re-orientation along the trail. The acquisition of odor cues is heavily
guided by active sampling via sniffing and body movements, which introduces a strong coupling
between sensation and motor actions. Theoretical studies hint at multi-modal strategies based
on bilateral sampling, temporal integration and the use of internal models, whose relative
contributions remain unclear. Here, a team of three PIs with complementary expertise, proposes
to dissect the algorithmic and neural basis of olfactory trail tracking, which can offer deeper
insights into active sensation, spatial navigation and continuous decision making. Using
behavioral, physiological, molecular and analytical methods, the PIs will test algorithmic
hypotheses and identify neural circuits guided by the following aims. In Aim 1, they will
investigate the strategies exhibited by mice during trail tracking and identify brain regions
supporting this behavior. A high-throughput adaptive system will be used to characterize the
behavior of mice while tracking odor trails in a custom-built treadmill. In Aim 2, the PIs will
uncover the neural circuits and cell types in brain regions involved in trail tracking. They will use
cell-type targeted measurement of neural activity, viral tracing and transcriptomics in olfactory
cortical areas to uncover patterns of activity and neural connectivity supporting neural
computations necessary for trail tracking. In Aim 3, the PIs will elucidate, theoretically and
computationally, behavioral strategies that mice use to track odor trails, and their underlying
neural algorithms. They will use experimental data of Aim 1 to assess the validity of a novel
theoretical framework, specifically in the context of sector search strategies and bilateral
processing by rodents. Experimental data of Aim 2 will be used to unveil the neural dynamics
and connectivity of sub-circuits that implement the algorithms driving behavior.

## Key facts

- **NIH application ID:** 10524245
- **Project number:** 1RF1NS128865-01
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** Catherine Dulac
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,730,261
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10524245

## Citation

> US National Institutes of Health, RePORTER application 10524245, Odor trail tracking: a new paradigm to unveil algorithms and neural circuits underlying active sensation and continuous decision making (1RF1NS128865-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10524245. Licensed CC0.

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