# Elucidating the Neural Computations Underlying Spatial Learning, Decision-Making and Generalization in Virtually-Navigating Monkeys

> **NIH NIH K99** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $125,442

## Abstract

Project Summary/Abstract
 Patients with Alzheimer’s disease and Alzheimer’s disease-related dementias exhibit symptoms that include
deficits in spatial navigation, and in forming/using cognitive maps of space to guide decision-making, especially
in new environmental situations. These deficits are linked to disease-related perturbation of medial temporal lobe
and frontal lobe brain structures, but surprisingly little is known about the underlying computations that go awry.
 Spatial navigation is believed to be dependent on populations of neurons in the hippocampus with “place-cell”
like representations. Decision-making in novel situations is dependent on representations of latent features that
are shared across examples or experiences, as observed in prefrontal cortex. Finally, the output of these
computations must reach the motor cortex where neural activity is coupled to immediate behavior. However,
studies to determine whether/how neural computations across these brain regions contribute to decisions based
on spatial location, especially in novel situations have not been conducted. Furthermore, whether symptoms of
Alzheimer’s disease and related dementias can be treated by modulating these computations is not known.
 To address this gap, I created an innovative new virtual reality paradigm and trained monkeys to make
decisions about which objects to collect based on learned spatial rules. I will combine this with high-channel-
count electrophysiology in trained monkeys to determine if/how population-level representations in the HPC-
PFC-PMd support decision-making based on spatial rules. Then, I will elucidate if and how population activity in
the HPC-PFC-PMd circuit supports generalization in novel situations. In my independent laboratory, I will use
this model system as a platform for elucidating the neural mechanism of an emerging treatment for Alzheimer’s
disease and Alzheimer’s disease-related dementias. These contributions are significant because they will
provide new insights into the neural mechanisms of some of our most adaptive cognitive capacities, while also
creating a new platform for discovering and testing new treatments where I systematically test the effects of
neural perturbations on neural circuit function and behavior.
 This project will facilitate my training as an independent researcher through new experience in high-channel
count, multi-area recordings, training in the etiology and treatment of Alzheimer’s disease and related dementias,
and in running a monkey electrophysiology lab at the intersection of basic and translational neuroscience. This
project will result in direct interactions between experimental neurophysiologists, theoretical neuroscientists, and
clinician-scientists. This award will help me achieve my long-term career goal to run an independent research
laboratory at an academic institution with a medical school, where I will operate a monkey electrophysiology lab
at the nexus of 1) elucidating neural mech...

## Key facts

- **NIH application ID:** 10877081
- **Project number:** 5K99NS133475-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Roberto Adamo Gulli
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $125,442
- **Award type:** 5
- **Project period:** 2023-07-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877081

## Citation

> US National Institutes of Health, RePORTER application 10877081, Elucidating the Neural Computations Underlying Spatial Learning, Decision-Making and Generalization in Virtually-Navigating Monkeys (5K99NS133475-02). Retrieved via AI Analytics 2026-07-20 from https://api.ai-analytics.org/grant/nih/10877081. Licensed CC0.

---

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