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Study design
Researchers analyzed motor-cortex recordings from two rhesus macaques learning to guide a cursor with a brain-computer interface. They compared learning across sessions using adaptive versus fixed decoder mappings and built a recurrent neural-network model to examine how decoder changes might influence learning.
What the findings show
With adaptive decoders, task-relevant information became concentrated in a smaller subset of neurons and population activity modes over practice. This compaction was not seen in the limited fixed-decoder comparisons. The model suggested that decoder adaptation itself can contribute to the changed representation, potentially by altering the error signals used in learning.
Limits
The physiological experiments were in two monkeys using a cursor task; the model is explanatory and does not prove the same process in human users. The work concerns neural learning during BCI control, not clinical outcomes or a demonstrated improvement in assistive-device usability.
This sourced news summary and translation were prepared with AI assistance. Source links, the news date and research limitations are disclosed; this is not a claim of independent medical expert review. News production method.
Sources
- Assistive algorithms influence neural representations in motor brain-computer interfaces — Nature Communications ·
Research news is not medical advice or a recommendation to use a substance. Read findings in the context of the study design, participants and limitations. Editorial standards.
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