# Neural basis of planning

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $825,618

## Abstract

Project Summary/Abstract
Prospectively selecting an appropriate sequence of actions from a large number of alternatives requires
extensive computation. Although behavioral and modeling studies have advanced our understanding of how
the internal model of the animal’s environment might be interrogated to evaluate the outcomes of future
actions, the biological mechanisms of such computation remain poorly understood. In addition, new techniques
to monitor the activity of many neurons in multiple brain areas are enabling researchers to systematically test
theories of how temporally extended computations for high-order cognition like planning might be subserved by
broadly distributed neural activity. To capture these exciting opportunities, we have chosen to study the neural
basis of planning during the four-in-a-row task, which is both rich enough to keep the essential elements of
complex planning and tractable for rigorous computational and neurophysiological investigations. The objective
of the four-in-a row task is to place four consecutive stones in a grid before the opponent does so. This task
allows us to study the neural processes for iteratively evaluating possible outcomes or values of alternative
action sequences across many well-defined states. Recent work has characterized the computational
strategies of humans in this task and how they are refined during practice. In addition, our preliminary studies
show that non-human primates can be trained to perform the same task. In Aim 1, we will apply rigorous
statistical and machine learning techniques to understand how the animals use feature-based evaluation and
potentially tree search to make a move. In Aims 2 and 3, we will examine how computational aspects of
planning rely on the coordination between neural populations in fronto-striatal and fronto-hippocampal
networks. In Aim 2, we will simultaneously record the activity of many neurons in the dorsolateral and
dorsomedial prefrontal cortex and in the caudate nucleus to test how these regions are involved in integrating
various task features during action selection. In Aim 3, we will simultaneously record from the prefrontal cortex
and hippocampus to test whether dynamic changes in the state representation in the HPC are reflected by
successive transformation of neural signals related to values and actions in the PFC. We will also test whether
sequential moves decoded from neural activity in the HPFC and PFC during replays correspond to past and
future choices made by the animal. The results from these experiments will improve our understanding of how
the brain develops good plans adaptively.

## Key facts

- **NIH application ID:** 10942626
- **Project number:** 1R01MH137210-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** DAEYEOL LEE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $825,618
- **Award type:** 1
- **Project period:** 2024-06-05 → 2029-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10942626

## Citation

> US National Institutes of Health, RePORTER application 10942626, Neural basis of planning (1R01MH137210-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10942626. Licensed CC0.

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