# Cortical Control of Motor Learning

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $386,431

## Abstract

Project Summary
The ability to learn from experience is one of the most fundamental features of neural circuits. Changes in
synaptic connections in specific circuits underlie experience-dependent circuit modifications essential for
learning. A detailed understanding of this process is important, not just to understand the mechanisms of
learning, but also to better diagnose and treat conditions that affect memory abilities, such as Alzheimer's
disease, aging-related dementia, and Parkinson's disease. Our ultimate goal is to understand the precise, fine-
scale circuit modifications that support learning.
 One of the fundamental forms of learning is motor learning in which animals adjust the way they move
their bodies to fit their behavioral goals. Among a number of brain areas involved in motor learning, the primary
motor cortex (M1) is a major locus where changes take place during motor learning. Many types of changes in
M1 have been described that accompany motor learning, including changes of the somatotopic map, neural
population activity changes, and synaptic plasticity. However, it is unclear whether M1 is always involved in the
control of movements throughout learning and overtraining. Furthermore, the precise functional reorganization
of synaptic inputs in M1 during motor learning is only beginning to be understood. We will address these two
questions using cutting-edge technologies in mice. Mice under head-fixation will be trained in a forelimb-based
motor learning task daily over weeks. In Aim 1, we will perform longitudinal recording of M1 neural populations
during months of motor learning and overtraining. Combined with optogenetic perturbation of M1 activity at
various phases of training, we test the hypothesis that a movement that is dependent on M1 early in learning
can become M1-independent with long-term overtraining. This will also define the period during which the
particular motor task we use in the proposal depends critically on M1. Focusing on this period when M1 is
critical for motor performance, we will study precise functional reorganization of synapses in M1. We will do
this using longitudinal functional imaging at synaptic resolution. In particular, we will test the hypothesis that
motor learning induces functional clustering of synaptic inputs related to the learned movements. Such
functional clustering would allow the learning-related information to robustly drive circuit activation. These
experiments will contribute fundamental neural circuit mechanisms underlying motor learning. Such knowledge
could ultimately contribute to a better diagnosis and treatment of motor disorders such as Parkinson's disease
and stroke.

## Key facts

- **NIH application ID:** 10069402
- **Project number:** 5R01NS091010-07
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Takaki Komiyama
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $386,431
- **Award type:** 5
- **Project period:** 2015-02-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10069402

## Citation

> US National Institutes of Health, RePORTER application 10069402, Cortical Control of Motor Learning (5R01NS091010-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10069402. Licensed CC0.

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