# Computational Dynamics in Neural Populations of Freely Foraging vs. Restrained Monkeys

> **NIH NIH RF1** · NEW YORK UNIVERSITY · 2022 · $2,828,022

## Abstract

Summary
This proposal will investigate the neural dynamics underlying three-dimensional foraging behavior, with three
overarching goals. The first is to evaluate the neural computations of foraging in dynamic environments in
naturalistic settings. The second is to compare the algorithmic behavioral computations and their neural
substrates under naturalistic versus traditional laboratory settings. Most neuroscience studies assume that
laboratory behavior has real-world implications, so any differences we observe would have remarkable
consequences for the future of neuroscience. The third goal is to test the role of hippocampal formation, frontal,
and parietal areas in the different components of foraging. We will compare three behaviors in two experimental
setups. The behaviors are free-foraging for unpredictable, random rewards, a goal-driven foraging task with
navigation among three resource patches under visual uncertainty and stochastic reward presentation, and
foraging among three resource patches without navigation. The latter two tasks involve the formation and
maintenance of internal models, memories, time-dependent sensory and reward contingencies, the costs of an
animal’s own actions, and dynamically changing beliefs about the state of the world. The former two tasks will
be performed during head-free navigation in a foraging room, and during head-fixed navigation in virtual reality.
In both environments, we will record simultaneously from several mutually interconnected areas involved in visual
navigation, spatial memory, path integration, and decision-making. Target areas include posterior parietal cortex,
prefrontal cortex, retrosplenial cortex, entorhinal cortex, and hippocampus. We will use advanced behavioral
models and theory to infer internal states and to identify their neural representation and interactions across a
broad network of interconnected brain areas. In addition, we will regress neuronal activity against task-relevant
variables to identify neural subspaces that encode them. Collectively, we expect that these experiments will
rigorously illuminate the neural dynamics of foraging and spatial navigation behaviors and critically interrogate
whether and how constrained laboratory conditions with head-fixed, restrained animals differ from ecological and
naturalistic environments—a critical but still untested assumption of contemporary systems neuroscience.

## Key facts

- **NIH application ID:** 10447347
- **Project number:** 1RF1NS127122-01
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Najib Judeh Majaj
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,828,022
- **Award type:** 1
- **Project period:** 2022-04-15 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10447347

## Citation

> US National Institutes of Health, RePORTER application 10447347, Computational Dynamics in Neural Populations of Freely Foraging vs. Restrained Monkeys (1RF1NS127122-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10447347. Licensed CC0.

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