# Convergence of top-down and bottom-up thalamic inputs in medial entorhinal cortex

> **NIH NIH F32** · STANFORD UNIVERSITY · 2024 · $73,828

## Abstract

Learning and memory is profoundly impaired in patients with Temporal Lobe Epilepsy (TLE) and Alzheimer’s
Disease (AD), with devastating consequences on everyday actions such as the ability to safely navigate back
home. Temporal lobe structures such as the medial entorhinal cortex (MEC) and hippocampus are critical for
the brain’s ability to create, update, and use internal representations of a dynamic external world. While decades
of research have revealed how MEC neurons—whose firing rate and patterns encode an animal’s position,
orientation, and speed in the environment—can build a static, reliable “map” of the physical environment, less is
known about how these maps can be flexibly updated to meet changing behavioral demands. Specifically, how
are abstract cognitive features, such as having to avoid an accident-prone traffic intersection or pick up groceries
on the way home, integrated into neural maps of the environment to guide behavior? Better understanding the
neural circuits and computations which govern flexible spatial processing in MEC is a crucial step towards
identifying vulnerabilities in the entorhinal-hippocampal network which may be targeted to alleviate cognitive
impairments. Recent advances in high-density electrophysiological recording techniques have generated critical
insights about the organization and diversity of information encoded by MEC neurons. In parallel, advances in
3D video recordings techniques and machine-learning algorithms have unlocked access to the rich dynamics of
rodent behavior. In this proposal, we combine these state-of-the-art techniques to densely sample single-unit
neural activity and behavioral dynamics at high resolution in freely-moving mice performing tasks associated
with different cognitive demands, while deploying optogenetic perturbations and an unbiased statistical model of
neural encoding. This strategy will allow us to rigorously assess how diverse physical and abstract features of
the environment are encoded by hundreds of simultaneously recorded neurons across brain regions. Our
overarching hypothesis is that flexible transformations of MEC maps are shaped by distinct neural circuits
conveying physical versus abstract features of the environment. In Aim 1, we will test the hypothesis that changes
in cognitive demands transform neural maps by recruiting a diverse repertoire of spatial and behavioral variables
to be flexibly encoded by MEC neurons. In Aim 2, we will examine if changes in physical features of the
environment drive coordinated transformations of spatial representations across MEC and upstream regions
such as the anterior thalamic nucleus. In Aim 3, we will determine if cognitive signals conveyed by the thalamic
nucleus reuniens enable transformations of MEC spatial representations. This proposal will identify circuit-level
substrates that influence which, when, and how transformations of MEC representations occur. By revealing how
flexible MEC spatial processing is orchestra...

## Key facts

- **NIH application ID:** 10946969
- **Project number:** 1F32NS138225-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Frances Cho
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $73,828
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10946969

## Citation

> US National Institutes of Health, RePORTER application 10946969, Convergence of top-down and bottom-up thalamic inputs in medial entorhinal cortex (1F32NS138225-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10946969. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*