# Understanding Sensorimotor Control Through Realistic Neuro-Biomechanical Simulation

> **NIH NIH U01** · HARVARD UNIVERSITY · 2024 · $2,947,975

## Abstract

Summary/Abstract
The brain evolved to move the body, i.e., to implement sensorimotor control. Understanding the brain, then, is
inextricably linked to understanding how it coordinates the joints and muscles of the body to generate competent
behavior in dynamic and unpredictable environments in the service of goals. A tantilizing consequence of this is
that understanding sensorimotor control will shed light on overall brain organizational and computational
principles, including, potentially, higher-level cognition, which evolved much more recently by adapting the
circuits already in place to control movement. This promise has not yet been realized, however, because
standard approaches to studying motor control seek to reduce complexity by, for example, isolating simple
circuits, studying artificial tasks, or constraining movements. These approaches thus avoid the sine qua non of
motor control in biology: multiple interacting brain regions, multiple simultaneous goals, and multiple muscle
coordinations, all in the presence of many sources of noise and sensory delays. Here, we propose to embrace
this complexity rather than reduce it and are enabled to do so through the use of 'virtual rat' models that comprise
deep neural network controllers designed to be analogous to biological brains and biomechanically accurate
bodies that are instantiated in simulators with real physics. Using a high-throughput easy-to-use ‘virtual
neuroscience’ platform that we are developing for our own use, and the use of the broader research community,
we will train these models to imitate freely-behaving real animals such that they internalize the statistics of
naturalistic behavior and then train them to solve goal-directed tasks. This novel ‘deep neuroethology’ approach
has two crucial features: highly biologically realistic behavior and the full transparency of a model. We will then
apply this approach to generate and test longstanding hypotheses about motor control and learning. For
example, we will interrogate: (i) how the learning and execution of complex behavior are influenced by certain
circuit motifs such as laterality, reciprocal inhibition between antagonistic muscle pairs, feedback architecture,
sensor delays, cortical–subcortical interactions, and dopamine-mediated plasticity; (ii) how feedforward outputs
and feedback inputs – in the setting of noise and sensory delays – coordinate movement; (iii) how animals learn
to adapt their behaviors quickly such that they can generalize across novel environments and tasks; and (iv)
what roles the distinct neural representations and circuit motifs found throughout the hierarchy of the motor
system play in neural computation. The results of these studies will drive previously unachievable refinements
to our theories of sensorimotor control and will thus spur new research directions in motor neuroscience and,
potentially, in robotics and other fields. Finally, and perhaps most importantly, we will have demonstrated the
po...

## Key facts

- **NIH application ID:** 10869524
- **Project number:** 1U01NS136507-01
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** Bingni Wen Brunton
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,947,975
- **Award type:** 1
- **Project period:** 2024-09-05 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10869524

## Citation

> US National Institutes of Health, RePORTER application 10869524, Understanding Sensorimotor Control Through Realistic Neuro-Biomechanical Simulation (1U01NS136507-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10869524. Licensed CC0.

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