# Novel approaches for interrogating and manipulating synaptic function, structure and plasticity

> **NIH NIH R35** · UNIVERSITY OF COLORADO DENVER · 2021 · $533,594

## Abstract

Synapses throughout the central nervous system are sculpted by neural activity through
changes in their size, shape and molecular composition, which either strengthen or weaken
communication between neurons. This “plasticity” in synapse function is widely viewed as the
central mechanism for information storage in the brain. While many forms of synaptic plasticity
have been discovered and their molecular mechanisms intensely investigated, in many cases
there is surprisingly little direct evidence linking them to the cognitive functions they are
proposed to control. This has remained a challenge due to a lack of tools for rapidly and locally
switching on or off the requisite biochemistry and cell biology underlying different plasticity
mechanisms in real time, in vivo. We are developing new tools that fill this void with the long-
term goal of addressing fundamental gaps in our knowledge concerning how synapses are
modified at the molecular level through development and plasticity, how these modifications
influence synapse/circuit function and ultimately the relevance of these mechanisms for
important cognitive functions like learning and memory.

## Key facts

- **NIH application ID:** 10148832
- **Project number:** 5R35NS116879-02
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Matthew J Kennedy
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $533,594
- **Award type:** 5
- **Project period:** 2020-05-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10148832

## Citation

> US National Institutes of Health, RePORTER application 10148832, Novel approaches for interrogating and manipulating synaptic function, structure and plasticity (5R35NS116879-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10148832. Licensed CC0.

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