Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys
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Computer Science > Machine Learning
Title:Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2605.29355 [cs.LG] |
| (or arXiv:2605.29355v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29355
arXiv-issued DOI via DataCite (pending registration)
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