# Neural and Computational Architecture for Complex Navigation and Subjective Self-Location

> **NIH NIH K99** · NEW YORK UNIVERSITY · 2024 · $126,415

## Abstract

Project Summary
Candidate and Career Goals: I intend to become an independent scientist at a research-first academic institution,
bridging across levels of description (i.e., from computations to neurons) and furthering our understanding of
how the brain infers the world around us, as well as ourselves within it. I trained as a cognitive scientist (winning
the Glushko Prize for best dissertation in Cognitive Science, worldwide), with a focus on understanding our sense
of self-location; where am “I” located in space. I am now training in systems neuroscience, developing expertise
in large-scale rodent neurophysiology and with a focus on the dynamic aspects of self-location; spatial
navigation. These experiences complement each other; from behavioral computations to single-units, and from
static to dynamic self-location. Environment and Career Development Plan: I am mentored by Dr. Dora Angelaki
(NYU, expertise in navigation) and co-mentored by Drs. David Schneider (NYU, rodent self-generated actions)
and Cristina Savin (NYU, data science). Further, I am a scientist member of the International Brain Lab, allowing
me the opportunity to leverage world-class expertise (22 labs) in rodent neurophysiology. My training during the
K99 will focus on model-based analyses of behavior and neurons during continuous and complex naturalistic
tasks, as well as lab management skills (i.e., personnel, grant-writing, communication). Research Plan: Spatial
navigation is central to adaptive behavior, underlying our ability to trade-off the exploitation of our current location
with the exploration of novel ones. Beautiful work has detailed a number of spatial codes (e.g., place and grid
cells) in the hippocampal formation, yet we (1) lack a normative framework accounting for the complexities of
natural navigation, (2) do not understand how spatial codes from the hippocampal formation interact with cortex,
and (3) have focused on understanding how we build internal models of the world around us, while neglecting
its starting point – ourselves. During the K99 phase of the award, I will develop a naturalistic navigation task in
virtual reality where rodents will be required to disambiguate complex signals. These animals will be trained to
integrate velocity signals derived from motion across their retina (i.e., optic flow) into a position estimate, in order
to path integrate to the location of a latent target. Then, they will be tested in a novel situation, one where optic
flow may be caused by self- and/or target-motion. I expect animals to behave in line with Bayesian Causal
Inference (BCI) – a canonical computation wherein estimation biases emerge during small, but not large, signal
disparities (i.e., when observers operate under the incorrect internal model). Further, I will broadly map neural
activity throughout the rodent’s brain during BCI by leveraging novel large-scale neurophysiology techniques.
During the R00 phase of the award, I will directly manipulate the s...

## Key facts

- **NIH application ID:** 10814991
- **Project number:** 5K99NS128075-02
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Jean-Paul Noel
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $126,415
- **Award type:** 5
- **Project period:** 2023-05-01 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10814991

## Citation

> US National Institutes of Health, RePORTER application 10814991, Neural and Computational Architecture for Complex Navigation and Subjective Self-Location (5K99NS128075-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10814991. Licensed CC0.

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