# Unraveling constraints on motor cortical activity exploration and shaping during structural skill learning using large-scale 2-photon imaging and holographic optogenetic stimulation

> **NIH NIH F32** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $70,310

## Abstract

Project Summary
When learning new skills, experience with previously-learned skills can facilitate faster learning by constraining
behavioral exploration and shaping, a concept known as “structural learning”. The motor cortex plays an
essential role in learning new skills, and its initially variable activity is shaped and consolidated over
learning. However, how previous experience modulates exploration and shaping of cortical network activity
to facilitate new skill learning is not well understood. When the brain learns to control a brain-machine interface
(BMI), cortical network activity exploration and shaping is broad (high-dimensional) in BMI-naïve subjects
and constrained (low-dimensional) in BMI-experienced subjects, suggesting the following hypothesis.
Hypothesis: Previous experience facilitates faster learning of new, related skills by constraining how motor
cortical network activity is explored and shaped, effectively reducing the number of neural parameters to learn.
The hypothesis’ prediction is that during faster learning of related skills, neural dimensionality will be decreased
and aligned with previously learned neural patterns. This project tests the prediction by leveraging novel
closed-loop paradigms, chronic large-scale 2-photon calcium imaging, high-dimensional data analysis,
and holographic optogenetic stimulation to study and manipulate the neural basis of structural skill
learning. First, the correspondence between structural learning of muscle patterns and cortical network activity
exploration and shaping will be studied using large-scale 2-photon calcium imaging. Second, to causally link
neural variance to learning neural patterns, a high-performance, calcium imaging-based BMI will be developed,
and the relationship between structural neuroprosthetic learning and neural exploration and shaping will be
analyzed. Finally, the structure of cortical network activity will be artificially shaped using holographic
optogenetic stimulation and tested on neuroprosthetic skill learning. The long-term objective of this proposal
integrates several core goals of the BRAIN initiative. The proposal will produce a dynamic picture of the
learning brain and demonstrate causality using BMIs and holographic optogenetic stimulation. This work’s
outcome will contribute conceptual principles underlying skill learning and memory and guide the design of BMI
systems to restore movement and assist learning.
Aim 1: Investigate the relationship between structural motor learning and cortical network activity exploration
and shaping using a novel motor task and large-scale 2-photon calcium imaging.
Aim 2: Investigate the relationship between structural neuroprosthetic learning and cortical network activity
exploration and shaping using a high-performance, calcium imaging-based BMI.
Aim 3: Artificially shape structure of cortical network activity using closed-loop holographic optogenetic
stimulation and test effect on neuroprosthetic learning.

## Key facts

- **NIH application ID:** 9996792
- **Project number:** 5F32MH118714-03
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Vivek Athalye
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $70,310
- **Award type:** 5
- **Project period:** 2018-09-16 → 2021-09-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9996792

## Citation

> US National Institutes of Health, RePORTER application 9996792, Unraveling constraints on motor cortical activity exploration and shaping during structural skill learning using large-scale 2-photon imaging and holographic optogenetic stimulation (5F32MH118714-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9996792. Licensed CC0.

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