# Distributed Neural Activity Patterns Underlying Practice-Based Learning

> **NIH NIH K99** · HARVARD MEDICAL SCHOOL · 2022 · $117,363

## Abstract

PROJECT SUMMARY / ABSTRACT
To survive, animals must learn appropriate associations between sensory cues and motor actions through a
process of trial and error. We expect that this learning will strengthen the synaptic connections between
neurons representing the sensory cue and neurons initiating the motor action. The strengthened synapses may
be direct synaptic connections between these neuronal populations or via systems intermediate between these
neurons, i.e., a “plastic brain circuit” or “pathway.” Synaptic plasticity has been observed in many different brain
areas, and the mechanisms are moderately well understood. However, we have struggled to identify which
plastic brain circuit underlies, specifically, the sensory cue-to-motor action association that is learned through
the process of trial and error. This is due, in part, to the fact that many brain areas undergo plastic changes
during learning, as the experience of learning recruits a variety of different cognitive processes, including
sensory detection, motor control, feedback, working memory and reinforcement learning -- cognitive processes
that all engage different brain areas and distributed networks. During my postdoc, I developed an approach to
assign these cognitive functions to different brain circuits for a case of trial and error learning in mice. The
approach involved an innovative behavior paradigm and optogenetic tools that are spatially and temporally
precise. Mice learned to associate the optogenetic activation of visual cortex (cue) with a forelimb reach to
grab a food pellet (motor action). As a result of my postdoc work, I now know which neurons in the brain
encode this cue and which are required to initiate this motor action. Therefore I am now equipped to identify
the plastic brain circuit underlying the learned association between this cue and this action. Here I propose to
study the brain circuit between the cue-encoding neurons and the neurons necessary to initiate the motor
action, in vivo while mice learn the cue-action association. I will study the flow of neural activity from the cue-
encoding neurons in the visual cortex to the neurons in the superior colliculus that are necessary to initiate the
motor action. In Aim 1, I will identify changes in the cued activity in visual cortex over learning. In Aim 2, I will
determine how activity in the superior colliculus changes over learning. In Aim 3, I will determine whether the
output of this pathway is sufficient to trigger the motor action after learning. Hence this work speaks directly to
a key goal of the Brain Initiative, to “demonstrate causal links between brain activity and behavior.” I will learn
in vivo two-photon imaging for Aim 1 under the guidance of Dr. Sabatini, an expert at this technique. Aims 2
and 3 will be conducted in the independent phase using in vivo electrophysiology, a technique with which I
have extensive experience. These experiments will help to identify a pathway from visual cortex to ...

## Key facts

- **NIH application ID:** 10447345
- **Project number:** 1K99MH127471-01A1
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Kimberly Reinhold
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $117,363
- **Award type:** 1
- **Project period:** 2022-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10447345

## Citation

> US National Institutes of Health, RePORTER application 10447345, Distributed Neural Activity Patterns Underlying Practice-Based Learning (1K99MH127471-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10447345. Licensed CC0.

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