# CRCNS: Switching antennal lobe dynamic regime via olfactory and mechanical signal

> **NIH NIH R01** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2024 · $348,911

## Abstract

PROJECT SUMMARY (See instructions):
Multimodal integration is a fundamental feature of animal nervous systems that allows them to extract 
useful information from complex environmental signals and guide behavior. A defect in this process may 
lead to communication disorders. However, there is currently limited understanding of this pervasive 
phenomenon. Due to its simplicity, the honeybee antennal lobe (AL) provides an excellent system for 
studying sensory integration. Chemical (i.e., odor) and mechanical (i.e., wind speed) information converge
within the AL, likely in service of two intermingled tasks facing honeybees - tracking highly turbulent odor 
plumes and discriminating odors. Both are critical for foraging success. This proposal seeks to: (1) 
determine the impact of mechanical input (wind speed) on AL odor responses and odor classification; (2) 
determine the functional roles and mechanisms of multisensory integration within the AL. We postulate that 
the AL can switch between two distinct dynamic regimes – an odor tracking regime (triggered by high 
mechanical input) and an odor discrimination regime (triggered by low mechanical input). In other words, 
input from one modality affects the coding scheme of the other. To test this hypothesis, our experimental 
work will entail a suite of electrophysiological experiments that disentangle the contributions of each 
modality to AL dynamics, determine the impact of mechanical input on correlations across the AL, and 
assess the dependence of AL odor classification on mechanical input. Computationally, we will construct a 
realistic, experimentally benchmarked spiking network model of the AL integrating mechanical and olfactory 
inputs, and use it to study the network mechanisms that underlie AL dynamics within the two postulated 
regimes. The model will be used to explore conceptual ideas and generate specific hypotheses that will be 
tested in subsequent experiments. Finally, we will incorporate the fundamental principles uncovered in our 
work into novel machine learning algorithms for solving multimodal problems. The PIs are excellently suited 
for the proposed work – Dr. Lei is an expert in olfaction and the electrophysiological studies of the AL, Dr. 
Patel has extensive experience in biologically realistic modeling of AL dynamics, and Dr. Bazhenov is an 
expert in computational neuroscience, data analysis and machine learning.

## Key facts

- **NIH application ID:** 10874485
- **Project number:** 5R01DC020892-03
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** MAKSIM V BAZHENOV
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $348,911
- **Award type:** 5
- **Project period:** 2022-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10874485

## Citation

> US National Institutes of Health, RePORTER application 10874485, CRCNS: Switching antennal lobe dynamic regime via olfactory and mechanical signal (5R01DC020892-03). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10874485. Licensed CC0.

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