Transitioning from global to local computational strategies during brain-machine interface learning
When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control (i.e., direct neurons) can display marked changes in their firing patterns during...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1371107/full |
| _version_ | 1797201262849032192 |
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| author | Nathaniel R. Bridges Matthew Stickle Karen A. Moxon |
| author_facet | Nathaniel R. Bridges Matthew Stickle Karen A. Moxon |
| author_sort | Nathaniel R. Bridges |
| collection | DOAJ |
| description | When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control (i.e., direct neurons) can display marked changes in their firing patterns during BMI learning. However, the extent of firing pattern changes in neurons not directly involved in BMI control (i.e., indirect neurons) remains unclear. To clarify this issue, we localized direct and indirect neurons to separate hemispheres in a task designed to bilaterally engage these hemispheres while animals learned to control the position of a platform with their neural signals. Animals that learned to control the platform and improve their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. Animals that did not learn the BMI task did not shift from utilizing a global to a local strategy. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons. |
| first_indexed | 2024-04-24T07:44:45Z |
| format | Article |
| id | doaj.art-99394590f7744db38acd962ad46a3aea |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-24T07:44:45Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-99394590f7744db38acd962ad46a3aea2024-04-19T04:45:42ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-04-011810.3389/fnins.2024.13711071371107Transitioning from global to local computational strategies during brain-machine interface learningNathaniel R. Bridges0Matthew Stickle1Karen A. Moxon2Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, United StatesDepartment of Biomedical Engineering, University of California, Davis, Davis, CA, United StatesDepartment of Biomedical Engineering, University of California, Davis, Davis, CA, United StatesWhen learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control (i.e., direct neurons) can display marked changes in their firing patterns during BMI learning. However, the extent of firing pattern changes in neurons not directly involved in BMI control (i.e., indirect neurons) remains unclear. To clarify this issue, we localized direct and indirect neurons to separate hemispheres in a task designed to bilaterally engage these hemispheres while animals learned to control the position of a platform with their neural signals. Animals that learned to control the platform and improve their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. Animals that did not learn the BMI task did not shift from utilizing a global to a local strategy. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons.https://www.frontiersin.org/articles/10.3389/fnins.2024.1371107/fullposture controlbrain computer interfaceneuroprostheticlearningbrain machine interface (BMI) |
| spellingShingle | Nathaniel R. Bridges Matthew Stickle Karen A. Moxon Transitioning from global to local computational strategies during brain-machine interface learning Frontiers in Neuroscience posture control brain computer interface neuroprosthetic learning brain machine interface (BMI) |
| title | Transitioning from global to local computational strategies during brain-machine interface learning |
| title_full | Transitioning from global to local computational strategies during brain-machine interface learning |
| title_fullStr | Transitioning from global to local computational strategies during brain-machine interface learning |
| title_full_unstemmed | Transitioning from global to local computational strategies during brain-machine interface learning |
| title_short | Transitioning from global to local computational strategies during brain-machine interface learning |
| title_sort | transitioning from global to local computational strategies during brain machine interface learning |
| topic | posture control brain computer interface neuroprosthetic learning brain machine interface (BMI) |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1371107/full |
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