# The virtual rodent: a platform to study the artificial and biological control of natural behavior

> **NIH NIH F99** · HARVARD MEDICAL SCHOOL · 2022 · $35,574

## Abstract

Project Summary
Controlling complex bodies in uncertain environments is a challenge our brains have evolved to perfect, yet the
algorithms and neural network implementations that enable flexible and robust control have been difficult to
identify. This proposal is premised on the idea that progress will be served by embracing the complexities of the
underlying control systems, including the bodies they control and the diversity of animal behavior. To test this
idea and, more generally, provide a versatile platform for interrogating the neural circuit-level principles and
mechanisms underlying embodied motor control, I propose the virtual rodent. This in-silico animal will have a
body like a real rat, experience normal physics, and be trained to produce naturalistic rat behaviors. It will have
an artificial brain that can be fully interrogated, manipulated, and reconfigured. After establishing this platform, I
will develop an analysis approach to compare in-vivo neural activity from freely moving animals to the network
representations of the model. This endeavor expands upon recent approaches linking neural representations
with the representations of task-optimized artificial models in sensory systems, enabling the comparison of neural
activity with analytical models in the motor domain and during complex behavior. I then propose to further
develop the virtual rodent to probe questions related to hierarchical control and motor learning in animals and
machines.
In the F99 phase of this proposed research, I will continue to develop the virtual rodent as a platform to study
the artificial and biological control of natural behavior. Specifically, in Aim 1, I will finalize a behavioral
measurement, processing, and modeling pipeline to train artificial neural networks to imitate the behaviors of
real rodents while in a physical simulator, validate its performance, and demonstrate its utility as a model for
embodied motor control. In Aim 2, I will then record from motor centers of real rodents as they freely move and
compare their neural activity to the network activity of models enacting the same diverse movements.
In the K00 phase of this proposed research, I will expand upon the virtual rodent model to study hierarchical
control, a conserved feature of flexible and adaptive mammalian control. I will train an artificial neural network to
reuse lower-level control modules created as part of the F99 phase to autonomously solve motor tasks commonly
used in motor neuroscience research. This Aim is of great value to the field of motor neuroscience as it will
facilitate the comparison of neural activity of animals performing controlled tasks with the network activity of
analytical models performing physically simulated analogues of the same tasks. Together, these Aims offer a
new path in the study of the neural control of movement, one which embraces the complexity of behavior and
biomechanics to advance our understanding of flexible and adaptive motor contro...

## Key facts

- **NIH application ID:** 10540574
- **Project number:** 1F99NS125834-01A1
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Diego Etiony Aldarondo
- **Activity code:** F99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $35,574
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10540574

## Citation

> US National Institutes of Health, RePORTER application 10540574, The virtual rodent: a platform to study the artificial and biological control of natural behavior (1F99NS125834-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10540574. Licensed CC0.

---

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