# “CRCNS: Computational principles of memory based decision making in Drosophila”

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $197,696

## Abstract

Decision making largely depends on the integration of prior sensory experiences that are stored in the
brain as memories. One of the best-understood model systems of a two-choice decision process is
associative olfactory memory in Drosophila. In the fly, learning occurs at the synapse between Kenyon cells
(KCs) and mushroom body output neurons (MBONs). By pairing olfactory stimuli with a reward or a
punishment flies can reliably learn to distinguish two initially neutral odors. As in mammals, dopaminergic
neurons (DANs) represents this contextual valence and modulate the strength or connectivity of KC>MBON
synapses within distinct compartments of the MB, consequently altering behavior. To understand how this
decision making is encoded in memory we thus must first understand the computation performed within
individual DAN-KC-MBON modules. In preliminary work, we built a realistic computational model of the
MBON-3 neuron including the precise synaptic connectivity all 948 innervating KCs based on the complete
synaptic connectome of the MB. Our model incorporates precise membrane properties of the MBON-3
neuron that we obtained by performing patch-clamp recording in vivo. We demonstrate that MBON-3 is
electrotonically compact and show that activation of complex KC input patterns reflecting physiological
activation by individual odors in vivo are sufficient to robustly drive MBON spiking. Here, we will combine
experimental analysis in vivo with computational modelling to determine the mechanisms controlling MBON
activation under baseline conditions and in response to memory-induced plasticity. We will identify the
minimal number of KCs and KC synapses required for robustMBON activation for different subsets of
approach and avoidance MBONs in vivo. These data will guide computational approaches to identify
general and specific features of the KC-MBON interactions. The results will then serve to identify
compartment-specific plasticity mechanisms in vivo, and corresponding computational mechanisms in
silico. Finally, we extend these approaches to simultaneous analyses at multiple KC-MBON modules to
provide a computational basis for decision-making.

## Key facts

- **NIH application ID:** 10456950
- **Project number:** 5R01DC020123-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** ERNST NIEBUR
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $197,696
- **Award type:** 5
- **Project period:** 2021-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10456950

## Citation

> US National Institutes of Health, RePORTER application 10456950, “CRCNS: Computational principles of memory based decision making in Drosophila” (5R01DC020123-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10456950. Licensed CC0.

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