# Circuit dynamics supporting associative learning in the dentate gyrus

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $594,024

## Abstract

Project Summary
The brain transforms experiences into patterns of activity that control emotions, decisions and
behaviors. Discriminating these patterns of activity allow these experiences to be stored as
distinct entities, separating important stimuli from unimportant ones, and catalogued into
memory. Modern techniques in neuroscience such as large-scale recording, computational tools
for analysis of these datasets and circuit-based manipulations provide an opportunity for
developing a deeper understanding the mechanisms of learning and discrimination. These
efforts are of profound importance to human health, as the inability to encode experiences
appropriate precision is a hallmark of cognitive disorders associated with aging. One way that
neural circuits may discriminate experiences is by, at the population level, separating the neural
representations of these experiences with learning, allowing a readout area to better decode the
stimulus from the patterns of activity. A locus of this computation is the hippocampus (HPC),
which, with learning, encodes the relationships and distinctions between behaviorally relevant
variables. We have recently found the dentate gyrus subregion of the hippocampus classifies
cortical representations of olfactory stimuli, increasing the distance between odor
representations with learning. In this proposal, we aim to understand the mechanism by which
stimulus representations in the DG change with learning. In Aim 1, we will use viral,
electrophysiological and imaging tools to map the cell-types and networks that generate odor
representations in the DG. In Aim 2 we will determine the local circuit mechanisms that control
the flexibility of odor representations with learning, with a focus on dopamine-dependent
modulation of encoding dynamics in DG GCs. In Aim 3, we will determine how aging impacts
the flexibility neural representations in the DG, and how circuit-based manipulations can reverse
neural discrimination deficits in aged mice. Understanding the mechanisms that support
learning-induced flexibility of neural ensembles will facilitate the development of therapeutics for
the treatment of age-related cognitive decline.

## Key facts

- **NIH application ID:** 10436360
- **Project number:** 5R01DC019813-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Mazen A Kheirbek
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $594,024
- **Award type:** 5
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10436360

## Citation

> US National Institutes of Health, RePORTER application 10436360, Circuit dynamics supporting associative learning in the dentate gyrus (5R01DC019813-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10436360. Licensed CC0.

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