# Circuit mechanisms underlying learned changes in persistent neural activity

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2020 · $804,160

## Abstract

ABSTRACT
Persistent neural activity, a sustained response following brief stimuli that is observed in many
brain networks, needs to be appropriately tuned to meet the exacting demands of various motor
and cognitive tasks. One task that has been particularly amenable to understanding persistent
neural activity is the oculomotor control of gaze position. In the oculomotor system, where
persistent activity maintains the appropriate gaze angle necessary for high-acuity vision, this
tuning arises in large part through an interaction loop between the cerebellum and the oculomotor
neural integrator, a brainstem structure that temporally integrates eye velocity commands to
generate eye position encoding signals. Whereas we understand a fair bit about the coding
properties and signal transformations within the cerebellum and especially the neural integrator
proper, the interactions between these key brain centers is poorly understood. Here we aim to
close this gap through an integrative approach using a combination of whole-network two-photon
imaging, targeted optical perturbations, serial-section electron microscopy, in-vivo
electrophysiology, and next-generation network modeling. In Aim 1, we will combine volumetric
imaging of neuronal dynamics in the zebrafish with learning and targeted perturbations to examine
the causal relationships between cerebellar and integrator populations. In Aim 2, we will perform
high-resolution serial-section electron microscopy of a functionally imaged brain to gain insight
into the global circuit's anatomical connectivity. In Aim 3, we will directly fit a network model to
these dynamical and structural data to find the physiological interaction patterns in the circuit and
predict the sites of plasticity. In Aim 4, we will probe predicted sites of plasticity using cell-targeted
optical stimulations and whole-cell patch recordings. These tools and results will enable concrete
predictions about the circuit mechanisms tuning persistent neural activity, guide understanding of
the modular loops between the cerebellum and a wide range of other brain areas, and serve as
a benchmark for efforts to understand the dynamics and plasticity of interactions across the brain.

## Key facts

- **NIH application ID:** 9840947
- **Project number:** 5R01NS104926-03
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Emre R Aksay
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $804,160
- **Award type:** 5
- **Project period:** 2018-01-15 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9840947

## Citation

> US National Institutes of Health, RePORTER application 9840947, Circuit mechanisms underlying learned changes in persistent neural activity (5R01NS104926-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9840947. Licensed CC0.

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