Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866914612221313024 |
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| author | He, Jieshi Li, Puzhe Sui, Yanan Poo, Mu-ming |
| author_facet | He, Jieshi Li, Puzhe Sui, Yanan Poo, Mu-ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29355 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys He, Jieshi Li, Puzhe Sui, Yanan Poo, Mu-ming Machine Learning Neurons and Cognition 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. |
| title | Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys |
| topic | Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2605.29355 |