# Multiscale theory of synapse function with model reduction by machine learning

> **NIH NIH RF1** · UNIVERSITY OF CALIFORNIA-IRVINE · 2021 · $1,134,550

## Abstract

Project Summary/Abstract
 This project constructs a unifying model that links synaptic morphodynamics, the fundamental process of
learning and memory in the brain, to the underlying molecular signaling pathways that regulate it. The motivation
for this work is a new class of machine learning methods for multiscale modeling that are a promising candidate
for linking the disparate spatial and temporal scales involved, from s calcium events in nano-domains to actin
reorganization on the order of minutes across a dendritic spine head. Previously, it has only been possible to study
each of these scales in isolation. The project brings together experts in (1) modeling the biochemistry at synapses,
(2) modeling the growth of the actin cytoskeleton, and (3) developing the theory and algorithms of multiscale
modeling with machine learning. The result of this collaboration will be a milestone model in cellular neuroscience
that mechanistically connects calcium signaling in dendritic spines to the growth of the actin cytoskeleton in spine
remodeling. Currently, there are few models that can e ectively make predictions about actin structure formation
based on changes in calcium in ux into the post-synaptic spine. Since the new data-driven models will be more
computationally ecient than exact simulations, it will also be possible to incorporate them into coarse-scale
models of synapses used in network simulations and in neuroengineering applications. Additionally, the methods
developed in this work an important contribution to modeling in cellular neuroscience, particularly because they
are data-driven and therefore widely applicable. Finally, the development of a suite of software tools for multiscale
modeling with machine learning will catalyze future collaborations and scienti c developments in the neuroscience
community, particularly using models that aim to connect cellular phenomena with mechanisms at sub-second
resolution. Such models can potentially bene t the development of pharmaceutical targets for learning de cits
associated with aging and neurological disorders such as Alzheimers.

## Key facts

- **NIH application ID:** 10263653
- **Project number:** 1RF1DA055668-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** ERIC D MJOLSNESS
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,134,550
- **Award type:** 1
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10263653

## Citation

> US National Institutes of Health, RePORTER application 10263653, Multiscale theory of synapse function with model reduction by machine learning (1RF1DA055668-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10263653. Licensed CC0.

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