# Deconstructing Functional Circuits of Motor Cortex During Motor Learning

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $541,554

## Abstract

Generating adaptive body movements is a fundamental function of the brain. The primary motor cortex (M1) is
a central locus for motor learning and execution. M1 receives long-range inputs from several brain areas and
processes this information through local recurrent connections. However, how various inputs and local
connections work together to shape M1 activity is unknown. In this proposal, we will investigate the
contributions of long-range inputs and local connections to M1 activity and how they are shaped during motor
learning. We will do this using a well-established motor learning paradigm in mice. Our central hypothesis is
that the functional properties of individual M1 neurons are defined by their responses to long-range inputs and
local neurons. We further hypothesize that learning induces a reorganization of the responses of M1 neurons
to specific inputs. This proposal will investigate the nature of the information that M1 receives from various
upstream regions, the unique activity patterns of M1 neurons that receive these inputs, and how the local M1
network contributes to their activity patterns.

## Key facts

- **NIH application ID:** 10624891
- **Project number:** 5R01NS125298-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Takaki Komiyama
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $541,554
- **Award type:** 5
- **Project period:** 2022-06-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10624891

## Citation

> US National Institutes of Health, RePORTER application 10624891, Deconstructing Functional Circuits of Motor Cortex During Motor Learning (5R01NS125298-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10624891. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*