# Mechanisms of persistent neural activity

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2022 · $534,073

## Abstract

PROJECT SUMMARY / ABSTRACT
Although many well-studied aspects of neural function involve activity driven by sensory inputs or occurring at
the time of motor actions, the brain often links such fleeting sensory and motor signals with persistent activity.
Somehow, neural circuits and individual neurons are capable of maintaining activity without additional input.
This is a fundamental aspect of neural function and a critical building block of cognition. The mechanisms
underlying persistent neural activity have long been considered in both experiment and theory, but there is little
definitive mechanistic understanding of the circuit and cellular contributions to persistent activity. Indeed,
theories of persistent activity are far more biologically nuanced than current empirical knowledge— especially
in the nonhuman primate, from which our understanding should have greatest clinical relevance given the
number of disorders that involve persistent activity. Here, we propose work that leverages advanced
techniques for multiple scales (and specificities) of neural recordings with corresponding analyses of large-
scale datasets to test detailed theories of how the brain generates and maintains persistent activity.
Specific Aim 1. Establish the marmoset as a powerful complementary model system for dissecting
persistent activity mechanisms in primate brains.
We will demonstrate the viability of studying memory-guided saccades and persistent activity in the marmoset,
using successful training approaches, electrophysiology, and calcium imaging to elicit the key behavior and to
characterize the important brain areas in this exciting primate model system.
Specific Aim 2. Characterize the large-scale circuitry underlying oculomotor persistent activity.
Using large scale recordings of extracellular activity across multiple brain regions collecting during
performance of a memory-guided saccade task, we will acquire a dataset of unprecedented scale to assess
the large-scale circuitry underlying persistent activity. We will adapt, develop, and deploy advanced statistical
models to capture the functional interactions between neurons and brain areas.
Specific Aim 3. Test and refine theories of persistent activity with novel measurements at fine spatial
and genetic resolution.
We will perform both 2-photon imaging and high density electrophysiological measures of neural activity. The
imaging will allow us to test the local circuit components of the theory, as well as to assess cell-type-specific
contributions to persistent activity. High density electrophysiology will reveal the local circuit architecture and
signal flow that are not accessible with coarser techniques. Integrated within our analysis framework, the
resultant model of persistent activity will be supported and refined by multiple scales and forms of empirical
evidence, all collected in the primate brain.

## Key facts

- **NIH application ID:** 10467871
- **Project number:** 1R01EY033064-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Alexander C Huk
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $534,073
- **Award type:** 1
- **Project period:** 2022-07-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10467871

## Citation

> US National Institutes of Health, RePORTER application 10467871, Mechanisms of persistent neural activity (1R01EY033064-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10467871. Licensed CC0.

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