# Development of predictive coding networks for spatial navigation

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $418,750

## Abstract

Development of predictive coding networks for spatial navigation
Summary: Mammalian navigation uses internal models to predict the spatial-temporal statistical regularity of
the sequence of environmental locations. Predictive coding theories view the brain as Bayesian interpreter that
computes the difference between the external stimuli and an internal model of the world. It is increasingly
understood that sequential spatial information is represented by temporal sequences of ensemble neuronal
firing in the hippocampus. Without exception, these ensemble patterns have been investigated exclusively in
adult animals. A small handful of studies measured the activity of individual hippocampal neurons as pre-
weanling or juvenile animals briefly explored open-field environments. However, spatial experience is believed
to be internally represented by highly compressed temporal sequences of neuronal ensemble firing during
awake and sleep states in the form of theta sequences, replay, and preplay, which are expressed within
populations rather than single neurons. Our goal is to reveal the principles and stages of early-life development
and maturation of attractor-based compressed temporal sequences as priors for encoding future navigation
experiences (i.e., predictive coding), and the role age and early spatial experience play in these processes and
in navigation learning. To achieve this goal, we develop new methods to: 1. Chronically record, at millisecond
resolution, from large ensembles of neurons (up to 70 simultaneously) from the hippocampus in developing
freely-behaving and sleeping rats from first day after eye opening; 2. Reveal and analyze predictive coding
network properties; 3. Control and restrict animals’ prior spatial experience. Successful completion of this
proposal will provide unique resources to help understand the emergence and maturation of cortical networks
for internally-generated representations and will provide links and predictive models to study perturbations in
neuronal ensemble patterns underlying neurodevelopmental and psychiatric disorders like schizophrenia and
autism spectrum disorder.

## Key facts

- **NIH application ID:** 9830701
- **Project number:** 5R01NS104917-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** GEORGE DRAGOI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $418,750
- **Award type:** 5
- **Project period:** 2017-12-15 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9830701

## Citation

> US National Institutes of Health, RePORTER application 9830701, Development of predictive coding networks for spatial navigation (5R01NS104917-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9830701. Licensed CC0.

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