# Prefrontal-Hippocampal Interactions during Model-Based Learning

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA BERKELEY · 2023 · $44,450

## Abstract

Project Summary
Dysfunction of the prefrontal cortex (PFC) and the hippocampus (HPC) has been implicated in many
neuropsychiatric disorders, including schizophrenia, major depression, and post-traumatic stress disorder. Many
of the behavioral symptoms of these disorders can be modeled as dysfunctional reinforcement learning (RL)
processes. For example, a failure to optimally balance goal-directed (“model-based”, or MB) and habitual
(“model-free”, or MF) control can explain rumination in OCD. An overarching goal of our research is to inform
the future development of devices that will interact with neural circuits in a principled way to treat neuropsychiatric
disorders. One impediment to this approach is that the neural coding in many of these circuits remains poorly
understood. The aim of the current grant is to investigate the neuronal properties of HPC and PFC, and how
these structures interact with each other.
The HPC-PFC circuit may play an important role for reward-based learning processes that depend on a model
of the environment. The HPC has long been associated with representing a ‘cognitive map’ that encodes the
structure of the environment. It is bidirectionally connected with medial PFC (mPFC), which has been implicated
in reward-based learning and value-based decision-making. Our hypothesis is that the HPC-PFC is critical for MB
RL, via the HPC representing a predictive map of task, and communicating this map to mPFC to allow value inferences
that guide behavior. Our theoretical construct for modeling this process is the successor representation (SR), which
learns a map of the task in parallel with reward contingencies, and then evaluates potential actions by integrating
the predictive map with the learned reward contingencies.
To test this hypothesis, we have developed an abstract foraging task that requires the subject to navigate a
hidden state space to find a reward. To solve this task optimally, the subject must engage in MB RL and develop
an internal map of the state space. This map allows the subject to store the location of the most recent reward
and then correctly select the necessary actions to reach the rewarded location. First, we will record from single
neurons in mPFC and HPC, then record simultaneously from mPFC and the hippocampus to examine how these
regions communicate with each other during MB RL.
Taken together, the results of this proposal will expand our understanding of the roles and interaction of HPC
and PFC in the primate brain. This knowledge will not only inform efforts to improve diagnostic tools in clinical
psychiatry but can also lay the groundwork for the development of neuroprosthetic devices that will interact with
neural circuits in a principled way to treat neuropsychiatric disorders.

## Key facts

- **NIH application ID:** 10672916
- **Project number:** 5F31MH131342-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Eric Hu
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $44,450
- **Award type:** 5
- **Project period:** 2022-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10672916

## Citation

> US National Institutes of Health, RePORTER application 10672916, Prefrontal-Hippocampal Interactions during Model-Based Learning (5F31MH131342-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10672916. Licensed CC0.

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