ArticleNeuron2019
Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex.
Article in Neuron, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
- An intracortical brain-machine interface based on macaque ventral premotor activity.Scientific reports · 2026Article
- Frontal and parietal planning signals encode adapted motor commands when learning to control a brain-computer interface.PLoS biology · 2025Article
- Sensorimotor environment but not task rule reconfigures population dynamics in rhesus monkey posterior parietal cortex.Nature communications · 2025Article
- A theory of brain-computer interface learning via low-dimensional control.bioRxiv : the preprint server for biology · 2025Article
- A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis.Nature medicine · 2025Article
- Closed-loop fMRI at the mesoscopic scale of columns and layers: Can we do it and why would we want to?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Advancements in brain-machine interfaces for application in the metaverse.Frontiers in neuroscience · 2024Review
- Neural Plasticity in Sensorimotor Brain-Machine Interfaces.Annual review of biomedical engineering · 2023Review
- Article
- From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control.Neuroscience bulletin · 2022Review
- Preserved cortical somatotopic and motor representations in tetraplegic humans.Current opinion in neurobiology · 2022Review
- Article
- Article
- Computation Through Neural Population Dynamics.Annual review of neuroscience · 2020Review
- Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity.Nature biomedical engineering · 2020Article
- Strategies and prospects of effective neural circuits reconstruction after spinal cord injury.Cell death & disease · 2020Review
- New neural activity patterns emerge with long-term learning.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Although animal studies provided significant insights in understanding the neural basis of learning and adaptation, they often cannot dissociate between different learning mechanisms due to the lack of verbal communication. To overcome this limitation, we examined the mechanisms of learning and its limits in a human intracortical brain-machine interface (BMI) paradigm. A tetraplegic participant controlled a 2D computer cursor by modulating single-neuron activity in the anterior intraparietal area (AIP). By perturbing the neuron-to-movement mapping, the participant learned to modulate the activity of the recorded neurons to solve the perturbations by adopting a target re-aiming strategy. However, when no cognitive strategies were adequate to produce correct responses, AIP failed to adapt to perturbations. These findings suggest that learning is constrained by the pre-existing neuronal structure, although it is possible that AIP needs more training time to learn to generate novel activity patterns when cognitive re-adaptation fails to solve the perturbations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.