# Sensorimotor learning through adjustments of cortical dynamics

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $387,750

## Abstract

Abstract
Extensive research spanning theory, psychophysics, and physiology has investigated how we rely on statistical
regularities in the environment to improve our sensorimotor behavior: (1) Bayesian theory has provided an
understanding of how one should take advantage of statistical regularities, (2) psychophysical experiments
have documented the impact of such regularities on behavior, and (3) electrophysiology experiments have
identified neural signals that reflect those regularities. An important consideration is that statistical properties of
the environment are rarely stable. Therefore, a most pressing and unresolved question at the frontier of this
interdisciplinary body of work is how malleable brain signals, through experience, gradually acquire information
about new environmental statistics. Here, we will tackle this problem by developing a sensorimotor behavioral
paradigm in the non-human primate model that demands adaptive statistical learning (Aim 1). We will use this
paradigm to test specific computationally-motivated hypotheses regarding how the structure and dynamics of
neural activity in candidate regions of the frontal cortex change throughout learning (Aim 2). Finally, we will use
a dynamical systems approach to analyze the laminar organization of learning signals in the frontal cortex to
tease apart functional sub-circuits with distinct input-output properties that support sensorimotor learning (Aim
3).

## Key facts

- **NIH application ID:** 10321910
- **Project number:** 5R01NS119519-02
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Mehrdad Jazayeri
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $387,750
- **Award type:** 5
- **Project period:** 2021-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10321910

## Citation

> US National Institutes of Health, RePORTER application 10321910, Sensorimotor learning through adjustments of cortical dynamics (5R01NS119519-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10321910. Licensed CC0.

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