# Neural circuit mechanisms for multisensory associative learning

> **NIH NIH R34** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $729,141

## Abstract

Project Summary
The brain uses sensory representations to assess risk and predict reward in order to adjust behavior. Per­
ception is a multisensory process. To make reliable predictions, it is advantageous for the brain to combine
more than one sensory modality to represent the world. In humans, as in many species, there is evidence for
sophisticated forms of learning, such as crossmodal enhancement, where the integration of multiple stimuli
from different modalities facilitates memory formation and/or improves discrimination. Because research
has primarily focused on studying our senses in isolation, many questions remain with regards to multisen­
sory learning. Are the rules of sensory representation in learning centers similar across sensory modalities?
What circuit mechanisms underlie non­linear representations of bimodal cues? How do these affect mul­
tisensory learning? To answer these questions, we must be able to probe and manipulate neural circuits
at the site of multisensory integration and learning, which is challenging in many model organisms. Here
we propose to leverage a recent synaptic connectivity map of the mushroom body, a well­studied learning
center of the fruit fly Drosophila melanogaster, combined with state of the art in vivo imaging and genetic
manipulations techniques to accomplish this. The mushroom body has been almost exclusively studied in
the context of olfactory learning. However recent connectomics data has revealed that it receives a large
fraction of visual inputs. We will determine what kind and how visual information is represented in the princi­
pal cells of the MB (Aim1). We will then extend this characterization to compound visual/olfactory stimuli and
characterize circuit mechanisms for nonlinear interactions between these types of information (Aim2). With
this knowledge, we will determine stimulus parameters likely to elicit robust multisensory learning and use
these in a learning assay under the microscope to probe neural circuitry for multisensory learning (Aim3).
This project provide the foundation for a subsequent TargetedBCP R01 aimed at expanding our integrated
experimental and theoretical approaches to extract fundamental principles of multisensory learning.

## Key facts

- **NIH application ID:** 10524400
- **Project number:** 1R34NS128874-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Roudabeh Behnia
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $729,141
- **Award type:** 1
- **Project period:** 2022-07-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10524400

## Citation

> US National Institutes of Health, RePORTER application 10524400, Neural circuit mechanisms for multisensory associative learning (1R34NS128874-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10524400. Licensed CC0.

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