# Research Project 2 - Internal state dynamics of primate perception and decision-making

> **NIH NIH U19** · STANFORD UNIVERSITY · 2022 · $60,000

## Abstract

Research Project 2 - Internal state dynamics of primate perception and decision-making
 Lead: Eyal Seidemann PhD
 Project Summary
The overarching goal of RP2 is to understand how neural circuits in primate primary visual cortex (V1) interact
with other brain regions to give rise to behavioral performance in demanding visual discrimination tasks. We
will develop and use an unprecedented array of experimental tools for simultaneously reading and writing
neural population responses at multiple spatial scales in behaving macaque cortex. Our end goal is to develop
and test quantitative and mechanistic computational models that can account for (1) the encoding of external
inputs (visual or optogenetic) by dynamic V1 population responses, (2) the decoding of V1 responses to external
stimuli by neural populations in key downstream decision/motor areas that mediate behavior, and (3) the
interplay between spontaneous variations in V1 activity that are related to fluctuating internal states and their
impact on stimulus encoding and decoding. Together, our experimental and computational work will provide
novel insights into the mechanisms that mediate complex, naturalistic, perceptually guided behaviors in the
NHP brain.

## Key facts

- **NIH application ID:** 10698371
- **Project number:** 3U19NS118284-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** EYAL J SEIDEMANN
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $60,000
- **Award type:** 3
- **Project period:** 2021-09-17 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10698371

## Citation

> US National Institutes of Health, RePORTER application 10698371, Research Project 2 - Internal state dynamics of primate perception and decision-making (3U19NS118284-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10698371. Licensed CC0.

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