# CRCNS: Integrating sensory and prior information to control behavior

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2020 · $270,765

## Abstract

A fundamental goal of systems neuroscience is to describe how sensory inputs are integrated and guide an
animal's behavior. To be able to integrate these inputs, early sensory systems have developed selectivities
for specific stimulus features that allow them to analyze the inputs using these features as basis. We aim to
uncover how disparate motion signals are integrated to produce a global percept of motion, and to
understand the conditions in which such integration fails. Our proposal reflects the fact that adaptive
behaviors in complex environments face numerous challenges, from processing noisy and uncertain visual
motion information to predict future events on target trajectory contingencies and its interactions with a
dynamic, cluttered environment.
We propose to use dynamic inference as an efficient theoretical framework to understand how the brain
integrates Prior knowledges elaborated from statistical regularities of natural environments with different
sources of information across different time scales in order to extract relevant motion information from the
sensory flow and predict future events or actions. The smooth pursuit system is an excellent probe of such
hierarchical dynamical inferences from target motion computation to target trajectory prediction. In
marmosets, we have access to populations of neurons in pivotal cortical areas along the occipito-parieto-
frontal network that have been identified in non-human and human primates. We seek to uncover a unifying
empirical and theoretical framework to capture inference across different time scales.
RELEVANCE (See instructions):
We will examine how incoming sensory signals interact with prior experiences to guide behavior, using
dynamic inference as a theoretical framework. This study uses a specific tracking behavior (smooth pursuit)
to shed light on the fundamental problem of how the coordinated activity of large populations of sensory
neurons is parsed and converted into appropriate behaviors in the face of changing contexts, uncertainty,
and noise, a process disrupted in neurological disorders such as schizophrenia.

## Key facts

- **NIH application ID:** 10152829
- **Project number:** 1R01NS120562-01
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Nicholas J Priebe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $270,765
- **Award type:** 1
- **Project period:** 2020-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10152829

## Citation

> US National Institutes of Health, RePORTER application 10152829, CRCNS: Integrating sensory and prior information to control behavior (1R01NS120562-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10152829. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*