# Elucidating Principles of Sensorimotor Control using Deep Learning

> **NIH NIH RF1** · UNIVERSITY OF FLORIDA · 2022 · $27,900

## Abstract

Project Summary
How do distributed neural circuits drive purposeful movements from the complex musculoskeletal system? This
understanding and characterization will be critical towards the application of principled neurostimulation to
specific brain regions to study the effect of neural circuit perturbations on behavior, and conversely towards
predictions of the neural activity during perturbations in the behavior. The research objective of this BRAIN
Initiative proposal is to develop biologically-inspired goal- and data- driven artificial intelligence methods to
elucidate the neurodynamical basis of sensorimotor control. The outcomes of this research program will
fundamentally impact our understanding of the neural circuits underlying sensorimotor control. The tools
developed herein will be disseminated for free use by the scientific community.
Through this BRAIN Initiative proposal, we will elucidate principles of sensorimotor control by incorporating
recorded neural data in succinct and interpretable biologically-inspired models of the relationships between the
measured biological data and the corresponding behavior. In Aims 1 and 2, we will design and disseminate a
comprehensive modeling framework that integrates large-scale neural and behavioral data with physics-based
modeling of the musculoskeletal system and neuroanatomical constraints. The neural data and neuroanatomical
constraints will be incorporated in recurrent neural network models that will achieve desired behavior through
biophysically-based musculoskeletal models using cutting-edge machine learning methods. The modeling
framework created here will provide a much-needed opportunity to design a virtual laboratory in Aim 3 that tests
the effect of neural stimulation in a feedback setting, and predicts the effect of unseen behavioral conditions and
behavioral perturbation on resulting neural activity.
Breakneck advances in hardware and machine learning techniques have led to vast improvements in our ability
to record and model large-scale multi-regional neural data. Our broad research goal is to advance the current
state-of-the-art for modeling the neural control of movements by incorporating large-scale measurements and
biological constraints into theoretical models of sensorimotor control. This is a critical step towards (a) elucidating
the computational role of neural activity from different brain regions in controlling complex behavior, (b) allowing
us to further refine theoretical models of movement generation based on data, and (c) understanding where and
how to stimulate the brain in order to efficiently apply neurostimulation for achieving desired behavior. With the
development and dissemination of these tools, we hope to enter an era where virtual laboratories are not just a
way to analyze previously performed experiments, but are integrated into experimental pipelines such that they
can be utilized to their full potential during large-scale neuroscience experiments.

## Key facts

- **NIH application ID:** 10488409
- **Project number:** 1RF1DA056377-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Shreya Saxena
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $27,900
- **Award type:** 1
- **Project period:** 2022-09-15 → 2023-07-01

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10488409

## Citation

> US National Institutes of Health, RePORTER application 10488409, Elucidating Principles of Sensorimotor Control using Deep Learning (1RF1DA056377-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10488409. Licensed CC0.

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