# Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys

arXiv: https://arxiv.org/abs/2605.29355 · HTML: https://mlsanity.com/paper/2605.29355/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 7 of 7)

- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment](https://mlsanity.com/paper/2505.04331/) (2025)
- [X-MoGen: Unified Motion Generation across Humans and Animals](https://mlsanity.com/paper/2508.05162/) (2025)
- [Multi-dimensional Neural Decoding with Orthogonal Representations for Brain-Computer Interfaces](https://mlsanity.com/paper/2508.08681/) (2025)
- [BigMaQ: A Big Macaque Motion and Animation Dataset Bridging Image and 3D Pose Representations](https://mlsanity.com/paper/2602.19874/) (2026)

## Abstract (arXiv)

Understanding how cortical activity represents natural whole-body behaviors in primates remains challenging. Limited by the diversity of movements and inaccessibility of large-scale neural representation of whole-body kinematics, previous motor decoding studies focused on constrained tasks and limited limb movements. Here, we present a neural-behavioral recording and modeling framework for freely moving monkeys, combining large-scale epidural cortical signals from distributed sensory- and motor-related areas with synchronized multi-view motion capture through a custom-made data collection platform. We reconstructed whole-body monkey kinematics and learned a compact behavior prior using an autoregressive encoder-decoder model. Conditioned on neural signals, the model decoded accurate and realistic whole-body movement without explicit physical constraints. Our results provide a novel proof-of-concept approach for decoding natural whole-body movements in primates using large-scale intracranial neural activity.