# Tracking the emergence of internal models

> **NIH NIH UF1** · UNIVERSITY OF WASHINGTON · 2022 · $6,206,423

## Abstract

Abstract
Central to human and animal cognition is the idea of internal models: an internal repository of
knowledge about the structure of the world and its affordances that enables prediction and
planning. The existence of such models is fundamental to experience. As we move through the
world, the raw instantaneous sensory information that we receive is highly impoverished and
dynamic relative to the rich, organized, stable and detailed nature of experience. Learned and
perhaps partially innate priors allow the maintenance of a veridical representation of the world
around us, and rapid selection and integration of important information relevant to a task. In this
proposal, we aim to probe the neural implementation of world models by recording from multiple
brain areas in primates as they navigate naturalistic environments. Through modeling alongside
analysis of coordinated recordings across multiple labs during a single sequence of complex
tasks, we will develop a holistic understanding of how such models develop, depend on active
engagement with the world, and influence perception. In our project, three macaque
neurophysiology labs, with distinct cutting-edge expertise in different brain regions and
technologies, will collaborate with computational neuroscientists with different expertise to
pursue overlapping aims. We propose a novel collaboration strategy in which all three macaque
neurophysiology labs will investigate a common navigation task, and record from different but
overlapping subsets of areas: inferotemporal cortex (IT), motor cortex (MC) and prefrontal
cortex (PFC). We focus initially on IT-HC (Tsao), MC-HC (Orsborne), and PFC-HC (Buffalo), on
the hypothesis that HC, by virtue of its anatomy, its essential function in episodic memory
formation, and its master role in orchestrating subjective experience, constitutes a central hub
for representing the brain’s internal model of the world. To help to interpret this new data, we will
develop and probe network models of predictive processing in collaboration with the analysis
and theory team. Fairhall, Mihalas, Rao, and Shea-Brown, with complementary expertise in
neural coding, predictive coding, and network dynamics, will interact to develop integrated
model frameworks and work with each experimental lab individually on data analysis. Major
questions we will address include: How does such a “world model” develop and how is it
represented in the brain? What are the consequences of world models for the dynamics of
sensory processing and behavior? How is learned structural knowledge integrated with self-
motion during exploration of an environment? How is this information retrieved during memory-
guided navigation to a goal?

## Key facts

- **NIH application ID:** 10429372
- **Project number:** 1UF1NS126485-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Elizabeth A Buffalo
- **Activity code:** UF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $6,206,423
- **Award type:** 1
- **Project period:** 2022-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10429372

## Citation

> US National Institutes of Health, RePORTER application 10429372, Tracking the emergence of internal models (1UF1NS126485-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10429372. Licensed CC0.

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