# Deep neuroethology of a virtual rodent

arXiv: https://arxiv.org/abs/1911.09451 · HTML: https://mlsanity.com/paper/1911.09451/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 23 of 23)

- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.13888/) (2020)
- [Better artificial intelligence does not mean better models of biology](https://mlsanity.com/paper/2504.16940/) (2025)
- [Measuring and modeling the motor system with machine learning](https://mlsanity.com/paper/2103.11775/) (2021)
- [Human-AI Shared Control via Policy Dissection](https://mlsanity.com/paper/2206.00152/) (2022)
- [Neural Circuit Architectural Priors for Embodied Control](https://mlsanity.com/paper/2201.05242/) (2022)
- [Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamics](https://mlsanity.com/paper/2506.00138/) (2025)
- [Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes](https://mlsanity.com/paper/2109.12434/) (2021)
- [Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning](https://mlsanity.com/paper/2112.02027/) (2021)
- [Neural Circuit Architectural Priors for Quadruped Locomotion](https://mlsanity.com/paper/2410.07174/) (2024)
- [MIMIC-MJX: Neuromechanical Emulation of Animal Behavior](https://mlsanity.com/paper/2511.20532/) (2025)
- [Inducing Functions through Reinforcement Learning without Task Specification](https://mlsanity.com/paper/2111.11647/) (2021)
- [A Computational Model of Learning Flexible Navigation in a Maze by Layout-Conforming Replay of Place Cells](https://mlsanity.com/paper/2209.08572/) (2022)
- [From Data-Fitting to Discovery: Interpreting the Neural Dynamics of Motor Control through Reinforcement Learning](https://mlsanity.com/paper/2305.11107/) (2023)
- [Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice](https://mlsanity.com/paper/2405.11457/) (2024)
- [Approximating the Manifold Structure of Attributed Incentive Salience from Large Scale Behavioural Data. A Representation Learning Approach Based on Artificial Neural Networks](https://mlsanity.com/paper/2108.01724/) (2021)
- [A Computational Theory of Learning Flexible Reward-Seeking Behavior with Place Cells](https://mlsanity.com/paper/2204.11843/) (2022)
- [A Population-Level Analysis of Neural Dynamics in Robust Legged Robots](https://mlsanity.com/paper/2306.15793/) (2023)
- [An introduction to reinforcement learning for neuroscience](https://mlsanity.com/paper/2311.07315/) (2023)
- [A computational approach to visual ecology with deep reinforcement learning](https://mlsanity.com/paper/2402.05266/) (2024)
- [Embodied sensorimotor control: computational modeling of the neural control of movement](https://mlsanity.com/paper/2509.14360/) (2025)
- [Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives](https://mlsanity.com/paper/2511.08436/) (2025)

## Abstract (arXiv)

Parallel developments in neuroscience and deep learning have led to mutually productive exchanges, pushing our understanding of real and artificial neural networks in sensory and cognitive systems. However, this interaction between fields is less developed in the study of motor control. In this work, we develop a virtual rodent as a platform for the grounded study of motor activity in artificial models of embodied control. We then use this platform to study motor activity across contexts by training a model to solve four complex tasks. Using methods familiar to neuroscientists, we describe the behavioral representations and algorithms employed by different layers of the network using a neuroethological approach to characterize motor activity relative to the rodent's behavior and goals. We find that the model uses two classes of representations which respectively encode the task-specific behavioral strategies and task-invariant behavioral kinematics. These representations are reflected in the sequential activity and population dynamics of neural subpopulations. Overall, the virtual rodent facilitates grounded collaborations between deep reinforcement learning and motor neuroscience.