# Activity-Dependent Tagging of Cerebellar Neurons for Studying Signal Processing and Learning

> **NIH NIH R21** · STANFORD UNIVERSITY · 2022 · $192,247

## Abstract

PROJECT SUMMARY
The goal of the proposed research is to implement state-of-the-art techniques for recording and manipulating
neurons based on their activity in the cerebellum, to dissect the computations performed by the cerebellum to
control eye movements. Vision is an active sense, and the accurate control of eye movements plays an essential
role in vision. The cerebellum plays a key role in the control of eye movements, and in the refinement of eye
movement accuracy and precision through oculomotor learning. It is known that the part of the cerebellum
controlling eye movements receives visual and vestibular sensory information as well as copies of the eye
movement commands, and presumably uses these sensory and motor signals to guide oculomotor performance
and its modification by learning. However, a number of technical challenges have limited our ability to study how
different sensory and motor signals are integrated in the cerebellum, and how the different signaling pathways
are each modified during learning to improve oculomotor performance. Two newly developed tools, CaMPARI
and Cal-Light, hold great promise to overcome some of the technical challenges that have limited studies of
cerebellar computation. These tools offer advanced precision in the ability to record and manipulate neurons
based on their activity during specific task conditions. We will (1) evaluate the efficacy of CAMPARI and Cal-light
for selectively targeting (“tagging”), subpopulations of cerebellar neurons in a task- and activity-dependent
manner, and (2) use these tools to dissect the computations implemented by the cerebellum during oculomotor
performance and oculomotor skill learning. The technical outcome of the proposed work will be a new set of
experimental approaches for studying the cerebellum, as well as new experimental strategies for studying
computation and learning in other neural circuits. The scientific outcome will be new insights about the
computations performed by the cerebellum on its sensory (visual and vestibular) and motor (efference copy)
inputs. Advances in understanding how the cerebellum supports accurate eye movements will provide
conceptual underpinning for developing more rational interventions for oculomotor disorders, and, more
generally for the wide array of disorders associated with cerebellar dysfunction.

## Key facts

- **NIH application ID:** 10319181
- **Project number:** 5R21EY031639-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Jennifer L Raymond
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $192,247
- **Award type:** 5
- **Project period:** 2021-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10319181

## Citation

> US National Institutes of Health, RePORTER application 10319181, Activity-Dependent Tagging of Cerebellar Neurons for Studying Signal Processing and Learning (5R21EY031639-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10319181. Licensed CC0.

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