# CRCNS: Refining computational models of motor sequence learning and execution

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $339,281

## Abstract

This proposal aims to experimentally and computationally study how the brain learns and generates
complex sequences of actions. Since behaviors emerge from the dynamic interplay of many brain areas,
computational models that permit the study of distributed circuits are important. Such models can unify
results across multiple experiments and brain areas to provide insights into neural circuit computations and,
importantly, to generate experimental predictions. To provide experimental constraints for such models, a
motor learning paradigm is needed in which subjects can be reliably trained, their behavior quantified, and
neural activity in relevant circuits measured and manipulated. The rodent motor sequence task developed
and studied in the Olveczky lab conforms to these criteria, and hence can serve to constrain, refine, and
arbitrate between different computational models, and serve as an experimental test-bed for their
predictions. The computational effort, led by the Escola lab, will start with an exploration of circuit models
consistent with available data. Further analysis of these models will generate competing predictions and
ideas for experiments that arbitrate between them. The goal of this collaboration is to arrive at a circuit-level
description of how complex motor sequences are learned and produced in the form of a biologically
plausible computational model that can be refined and updated as new experimental results arrive.
Recordings from neurons in the striatum (a major motor system structure) reveal the existence of "sequence
cells"-- neurons that are sparsely active and precisely time-locked to the motor output. Additionally, the
Olveczky lab showed that motor cortex is essential for learning the task, but can be lesioned without
impairing task execution. These results raise important questions: 1) Is sequence cell activity in the striatum
driving the learned motor sequences, and if so, is this dynamics generated independently of motor cortex?
2) How do other motor-related circuits contribute to striatal dynamics and ultimately behavior? 3) How do the
circuits controlling complex motor sequences learn, i.e. where are the sites of plasticity within these circuits
and what learning rules govern their adaptive reorganization? A computational modeling framework,
constrained by experimental results, which rigorously and quantitatively addresses these questions, will
advance our understanding of how the motor system produces learned motor sequences. This
understanding will provide a new framework within which to consider the pathogenesis of movement
disorders such as Parkinson's and Huntington's diseases.

## Key facts

- **NIH application ID:** 9934295
- **Project number:** 5R01NS105349-04
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Gary Sean Escola
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $339,281
- **Award type:** 5
- **Project period:** 2017-07-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9934295

## Citation

> US National Institutes of Health, RePORTER application 9934295, CRCNS: Refining computational models of motor sequence learning and execution (5R01NS105349-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9934295. Licensed CC0.

---

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