# CRCNS: Neural computations for continuous control in virtual reality foraging

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2022 · $394,606

## Abstract

Neuroscience has been able to gain major insights by relating measurements of neural activity to the
 brain’s sensory inputs and motor outputs. Yet most neural activity supports computations and cognitive
functions (‘thoughts’) that are not directly measurable by the experimenter. The investigators for the
 present proposal invented a novel method to model an animal's thoughts by combining eXplainable
 Artificial Intelligence (XAI) cognitive models for naturalistic tasks with measurements of the animal’s
 sensory inputs and behavioral outputs. This model, called Inverse Rational Control (IRC), infers the
internal model assumptions under which an animal's actions would be optimal. It then provides estimates
of time series of subjective beliefs about the world that are consistent with this internal model. These
estimates provide targets for a dimensionality reduction framework that assesses task-relevant
 computational dynamics within neural population activity. The investigators propose to use these analysis
 tools to find neural representations and transformations that implement these cognitive processes. They
will apply this to a complex, naturalistic task that they developed: catching fireflies in virtual reality. The
 monkeys they successfully trained to perform this task demonstrably weigh uncertainty, develop
 predictions and long-term strategies, and apply nonlinear dynamics — all computations that are
 fundamental for brain function. The investigators propose first to apply their method to analyze existing
behavioral data and neural recordings collected in a simple version of this task with a single target firefly.
 They will then collect new data on a multi-firefly version of the task, which incentivizes animals to make
and implement longer-term plans. To analyze this data, the investigators will generalize their approach to
 allow them to learn which compressed representations are selected by the animal as the foundation for
 their strategies. These results will be used to form predictions about neural computations that will be
tested using the electrophysiological data collected from multiple brain regions during this project. The
 results of this study will explain the computations required to perform a complex, strategic navigation task
 in the presence of uncertainty, and will demonstrate a new paradigm for understanding naturalistic brain
computations.
RELEVANCE (See instructions):
 This project will uncover the neural basis of cognitive processes in the primate brain that underlie spatial
 navigation, strategic planning, and behavioral control. It will demonstrate how a powerful new paradigm
 for understanding complex, natural brain computations can apply to a wide variety of tasks, to explain
 either adaptive or pathologically structured behavior. This will provide crucial guidance for understanding
 and improving disrupted human cognitive function.

## Key facts

- **NIH application ID:** 10445287
- **Project number:** 5R01NS120407-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Zachary Samuel Pitkow
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $394,606
- **Award type:** 5
- **Project period:** 2020-09-30 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445287

## Citation

> US National Institutes of Health, RePORTER application 10445287, CRCNS: Neural computations for continuous control in virtual reality foraging (5R01NS120407-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10445287. Licensed CC0.

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