# CRCNS: Deconstructing ... - Diversity Research Supplement - Michael Silverangel

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $57,596

## Abstract

Project Summary
Motor cortex historically has been viewed as a relatively stable region of the brain, with static cortical dynamics mapping to
particular movements. Recent work has questioned these assumptions, asserting that the brain’s internal map of the external
world drifts over time. This phenomena, known as representational drift, may provide insight into how the brain adapts to
learn new behavior. Modeling representational drift and establishing its connection to motor learning are the primary aims
of this proposal.
Currently, there is no standardized metric to track representational drift over time. Leveraging our repository of motor and
premotor neuronal recordings, we look to establish a common framework for calculating the rate at which this drift occurs.
To do this, we will apply unbiased, probabilistic methods (e.g., continuous-time hidden Markov models, switching linear
dynamical systems) that extract discrete latent states to the neural data. Models will be evaluated based on offline decoding
performance, and drift will be calculated by the transition rate across latent states.
After establishing a common modeling framework, we will analyze how drift rates change across various motor tasks and
levels of exertion. To accomplish this, we will employ our novel, unconstrained recording platform, which allows for the
simultaneous capture of markerless kinematics and wirelessly transmitted neural data. By analyzing the neural data collected
during diverse movements, we can assess how both overall levels activity and different types of behavior impact rates of
representational drift.
By determining how physical activity impacts representational drift, we can better understand the relationship between drift
and rates of motor learning. To do this we will study to cohorts recovering from motor deficit. One cohort will integrate drift-
enhancing activities during recovery and another will not. By monitoring rates of representational drift and motor recovery,
we can evaluate the effect that drift has on motor learning. This knowledge can then be leveraged to improve treatments for
those recovering from brain-related movement disorders, like stroke and Parkinson’s.

## Key facts

- **NIH application ID:** 11037766
- **Project number:** 3R01NS130789-03S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Scott Warren Linderman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $57,596
- **Award type:** 3
- **Project period:** 2022-07-15 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11037766

## Citation

> US National Institutes of Health, RePORTER application 11037766, CRCNS: Deconstructing ... - Diversity Research Supplement - Michael Silverangel (3R01NS130789-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11037766. Licensed CC0.

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