Identifying Distinct Neural Features between the Initial and Corrective Phases of Precise Reaching Using AutoLFADS Article Swipe
Wei-Hsien Lee
,
Brianna M. Karpowicz
,
Chethan Pandarinath
,
Adam G. Rouse
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1523/jneurosci.1224-23.2024
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1523/jneurosci.1224-23.2024
Many initial movements require subsequent corrective movements, but how the motor cortex transitions to make corrections and how similar the encoding is to initial movements is unclear. In our study, we explored how the brain's motor cortex signals both initial and corrective movements during a precision reaching task. We recorded a large population of neurons from two male rhesus macaques across multiple sessions to examine the neural firing rates during not only initial movements but also subsequent corrective movements. AutoLFADS, an autoencoder-based deep-learning model, was applied to provide a clearer picture of neurons’ activity on individual corrective movements across sessions. Decoding of reach velocity generalized poorly from initial to corrective submovements. Unlike initial movements, it was challenging to predict the velocity of corrective movements using traditional linear methods in a single, global neural space. We identified several locations in the neural space where corrective submovements originated after the initial reaches, signifying firing rates different than the baseline before initial movements. To improve corrective movement decoding, we demonstrate that a state-dependent decoder incorporating the population firing rates at the initiation of correction improved performance, highlighting the diverse neural features of corrective movements. In summary, we show neural differences between initial and corrective submovements and how the neural activity encodes specific combinations of velocity and position. These findings are inconsistent with assumptions that neural correlations with kinematic features are global and independent, emphasizing that traditional methods often fall short in describing these diverse neural processes for online corrective movements.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1523/jneurosci.1224-23.2024
- OA Status
- green
- Cited By
- 4
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4393235483Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1523/jneurosci.1224-23.2024Digital Object Identifier
- Title
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Identifying Distinct Neural Features between the Initial and Corrective Phases of Precise Reaching Using AutoLFADSWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-27Full publication date if available
- Authors
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Wei-Hsien Lee, Brianna M. Karpowicz, Chethan Pandarinath, Adam G. RouseList of authors in order
- Landing page
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https://doi.org/10.1523/jneurosci.1224-23.2024Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/11097258Direct OA link when available
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Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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