ReviewCurrent opinion in neurobiology2017
Parsing learning in networks using brain-machine interfaces.
Review in Current opinion in neurobiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Assistive algorithms influence neural representations in motor brain-computer interfaces.Nature communications · 2026Article
- Brain-computer interfaces: an engineering black-box swindle or a lone advance guided by deep learning.Frontiers in neuroscience · 2026Article
- Brain-computer interfaces as a causal probe for scientific inquiry.Trends in cognitive sciences · 2026Review
- Assistive sensory-motor perturbations influence learned neural representations.bioRxiv : the preprint server for biology · 2025Article
- Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity.Communications biology · 2025Article
- Neural populations are dynamic but constrained.Nature neuroscience · 2025Article
- Dynamical constraints on neural population activity.Nature neuroscience · 2025Article
- Learning leaves a memory trace in motor cortex.Current biology : CB · 2024Article
- Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity.Journal of neural engineering · 2024Article
- Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.PloS one · 2024Article
- Invariant neural dynamics drive commands to control different movements.Current biology : CB · 2023Article
- Neural Plasticity in Sensorimotor Brain-Machine Interfaces.Annual review of biomedical engineering · 2023Review
- Modeling multiscale causal interactions between spiking and field potential signals during behavior.Journal of neural engineering · 2022Article
- Review
- Improving scalability in systems neuroscience.Neuron · 2021Review
- Learning is shaped by abrupt changes in neural engagement.Nature neuroscience · 2021Article
- Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators.Sensors (Basel, Switzerland) · 2021Article
- Learning poly-synaptic paths with traveling waves.PLoS computational biology · 2021Article
- Multiregional communication and the channel modulation hypothesis.Current opinion in neurobiology · 2021Review
- Strategies and prospects of effective neural circuits reconstruction after spinal cord injury.Cell death & disease · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies.
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What OpenQuestion holds
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.