Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders.
Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putati...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012614 |
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| author | Max Grogan Kyle P Blum Yufei Wu J Alex Harston Lee E Miller A Aldo Faisal |
| author_facet | Max Grogan Kyle P Blum Yufei Wu J Alex Harston Lee E Miller A Aldo Faisal |
| author_sort | Max Grogan |
| collection | DOAJ |
| description | Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots. |
| format | Article |
| id | doaj-art-fade427cc19b48c8a770441abdad54ae |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-fade427cc19b48c8a770441abdad54ae2025-08-20T02:37:06ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101261410.1371/journal.pcbi.1012614Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders.Max GroganKyle P BlumYufei WuJ Alex HarstonLee E MillerA Aldo FaisalProprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.https://doi.org/10.1371/journal.pcbi.1012614 |
| spellingShingle | Max Grogan Kyle P Blum Yufei Wu J Alex Harston Lee E Miller A Aldo Faisal Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders. PLoS Computational Biology |
| title | Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders. |
| title_full | Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders. |
| title_fullStr | Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders. |
| title_full_unstemmed | Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders. |
| title_short | Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders. |
| title_sort | predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders |
| url | https://doi.org/10.1371/journal.pcbi.1012614 |
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