# Examining simultaneous encoding of local and remote space across distinct hippocampal subnetworks

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $41,990

## Abstract

Project Summary/Abstract
Current experiences, memories, and imagined futures all affect behavior. Evidence demonstrates that the
hippocampus (HPC) can represent past locations, present position, and possible future trajectories. The HPC,
however, is often referred to as a single structure capable of only one type of representation at a time, but the
HPC can be divided into subregions along its dorsoventral axis. There is evidence for different functional roles
of the dorsal and ventral HPC in spatial navigation, but recent gene expression studies suggest the
dorsoventral axis can be divided into at least three subregions: dorsal, intermediate, and ventral. In contrast to
these partitions, electrophysiology signatures evolve gradually along the HPC longitudinal axis, suggesting a
gradual evolution of function. Whether the dorsal HPC (dHPC) and intermediate HPC (iHPC) behave as
distinct networks as suggested by molecular subdivisions or as parts of a larger functional gradient is unknown.
Both subregions are known to represent space, and studies in the dHPC have shown that representations of
local and nonlocal space are temporally patterned around a regular oscillation in the field potential. This
oscillation is present but phase-shifted in the iHPC, so the temporal patterning of local and nonlocal
representations may differ from the patterning in the dHPC. This means that the HPC may simultaneously
represent past, present, and future locations, and if so, the two subregions should be viewed as distinct
networks that both contribute to encoding space. Through a combination of experimental and analytic
methods, this project tests the hypothesis that dHPC and iHPC are best understood as distinct subregions that
represent space noncoherently.
 We will test these hypotheses by recording simultaneously in the rat dHPC and iHPC during a spatial
navigation task and studying the structure and temporal organization of spatial representations (Aim 1) as well
as the characteristics of correlated activity across the two subregions using a model of network activity (Aim
2). This computational model serves as a complementary approach to understanding the nature of correlated
activity in the HPC; it is completely agnostic to what neurons may encode and has no free parameters to
adjust. By combining both experimental methods with cutting edge analytic techniques, the proposed aims
have the potential to uncover previously unknown richness of representations in the HPC that will give further
insight into how memories and simulations of the future are coordinated with present experiences. The
simultaneous existence of these representations may serve as a neural substrate for relating past, present,
and future experiences to one another across time.

## Key facts

- **NIH application ID:** 10234804
- **Project number:** 1F31MH126626-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Rhino Nevers
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $41,990
- **Award type:** 1
- **Project period:** 2021-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10234804

## Citation

> US National Institutes of Health, RePORTER application 10234804, Examining simultaneous encoding of local and remote space across distinct hippocampal subnetworks (1F31MH126626-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10234804. Licensed CC0.

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