# Relating structure and function in synapse-level wiring diagrams

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $1,176,110

## Abstract

Project summary:
Modern electron-microscopy (EM) imaging and analysis methods now permit the comprehensive reconstruction
of all neurons and synapses in large volumes of brain tissue or the entire brains of individual organisms. However,
relating this structure to function is difficult. The rapidly increasing scale of these datasets requires the develop-
ment of new quantitative techniques to address this challenge. This proposal describes a combined data analysis
and modeling approach that is informed by large-scale EM datasets collected by our experimental collaborators.
The methods we will develop extend the state of the art by incorporating multiple sources of information about
neuronal connectivity and function to explain structure in EM wiring diagrams. They also leverage recent advances
in recurrent neural network optimization to use this structure to constrain models of neural dynamics. Our aim is
both to develop general and scalable techniques to be used on the latest generation of datasets, as well as apply
these techniques to specific scientific questions about the organization of the Drosophila mushroom body, which
is a primary target of current reconstruction efforts.
Specific aims of the project include a number of subgoals, starting with the development of techniques to determine
the organizing principles of neuronal wiring given a connectivity graph defined by an EM dataset. Unlike standard
methods, we aim to leverage multiple modalities of information; for instance, connectivity, cell types, functional
data, spatial location, and synaptic weights, to perform this inference. Next, we will perform an analysis of the
mushroom body of the adult Drosophila melanogaster brain, a center for associative learning in insects. This
analysis will both inform the development of our methods and also address fundamental scientific questions about
the nature of stimulus representations in mushroom body Kenyon cells and the circuitry involved in learning.
Strong parallels between the organization of the mushroom body and the mammalian cerebellum suggest that
these efforts will lead to generalizable insights. Finally, we will integrate structural information with modeling of
neural dynamics. We will characterize to what extent structure can be used to build well-constrained models,
validating our approaches on datasets that involve characterization of connectivity through EM and recording of
neural activity through calcium imaging.
The proposed methods will be of interest for researchers working across many model organisms for which EM
reconstruction efforts have been completed or are currently underway. We expect that the methods will provide a
template for integrating structural information into modeling efforts across these varied systems.

## Key facts

- **NIH application ID:** 10006999
- **Project number:** 1R01EB029858-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Ashok Litwin-Kumar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,176,110
- **Award type:** 1
- **Project period:** 2020-09-15 → 2024-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10006999

## Citation

> US National Institutes of Health, RePORTER application 10006999, Relating structure and function in synapse-level wiring diagrams (1R01EB029858-01). Retrieved via AI Analytics 2026-06-15 from https://api.ai-analytics.org/grant/nih/10006999. Licensed CC0.

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