The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index.
Functional network reorganization during learning in a brain-computer interface paradigm
Open accessAbstract
Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Another similar, overstated extrapolation regards evidence from invasive and semi-invasive BCI, where learning and co-adaptation have been well documented [19,23,24,57–59].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [24]
Citations
Cites
The Organization of Behavior; A Neuropsychological Theory.
Attneave, F., B., M., & Hebb, D. O. (1950) · The American Journal of Psychology · doi:10.2307/1418888 · Cited by 5,146
The Organization of Behavior: A Neuropsychological Theory
(1950) · JAMA · doi:10.1001/jama.1950.02910470083028 · Cited by 4,272
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
Georgopoulos, A. P., Kalaska, J. F., Caminiti, R., & Massey, J. (1982) · Journal of Neuroscience · doi:10.1523/jneurosci.02-11-01527.1982 · Cited by 2,133
CORTICAL PLASTICITY: From Synapses to Maps
Buonomano, D. V., & Merzenich, M. M. (1998) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.21.1.149 · Cited by 2,083 · cited 1×
“f==bo+mcoso, [1]”
Steps toward Artificial Intelligence
Minsky, M. (1961) · Proceedings of the IRE · doi:10.1109/jrproc.1961.287775 · Cited by 1,796
The Handbook of Artificial Intelligence
Salveter, S. C. (1982) · Elsevier eBooks · doi:10.1016/c2013-0-07691-8 · Cited by 1,703
Cortical control of a prosthetic arm for self-feeding
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008) · Nature · doi:10.1038/nature06996 · Cited by 1,591
Self-citationDirect Cortical Control of 3D Neuroprosthetic Devices
Taylor, D. M., Tillery, S. I. H., & Schwartz, A. B. (2002) · Science · doi:10.1126/science.1070291 · Cited by 1,542
Self-citationReal-time prediction of hand trajectory by ensembles of cortical neurons in primates
Wessberg, J., Stambaugh, C. R., Kralik, J. D., Beck, P. D., Laubach, M., Chapin, J. K., Kim, J., Biggs, S. J., Srinivasan, M. A., & Nicolelis, M. A. L. (2000) · Nature · doi:10.1038/35042582 · Cited by 1,491
Flagged first, then by citations
2 of the 36 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–200 of 302
Wang, Y., Wang, F., Xu, K., Zhang, Q., Zhang, S., & Zheng, X. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2341275 · Cited by 51
Massimi, M., & McCoy, C. D. (2019) · Routledge eBooks · doi:10.4324/9781315145198 · Cited by 65
Neural Correlates of Skill Acquisition with a Cortical Brain–Machine Interface
Ganguly, K., & Carmena, J. M. (2010) · Journal of Motor Behavior · doi:10.1080/00222895.2010.526457 · Cited by 50
Parsing learning in networks using brain–machine interfaces
Orsborn, A. L., & Pesaran, B. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.002 · Cited by 46
Network neuroscience for optimizing brain–computer interfaces
Fallani, F. D. V., & Bassett, D. S. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.10.001 · Cited by 47
Brain-Inspired Motion Learning in Recurrent Neural Network With Emotion Modulation
Huang, X., Wu, W. L., Qiao, H., & Ji, Y. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2843563 · Cited by 46
Brain–Machine Interfaces: Basis and Advances
Becedas, J. (2012) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2203301 · Cited by 46
High-Fidelity Coding with Correlated Neurons
da Silveira, R. A., & Berry, M. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003970 · Cited by 46
Sakellaridi, S., Christopoulos, V. N., Aflalo, T., Pejsa, K., Rosario, E. R., Ouellette, D. S., Pouratian, N., & Andersen, R. A. (2019) · Neuron · doi:10.1016/j.neuron.2019.02.012 · Cited by 43
Learning, Generalization, and Scalability of Abstract Myoelectric Control
Dyson, M., Dupan, S. S. G., Jones, H., & Nazarpour, K. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3000310 · Cited by 43
Grand challenges of brain computer interfaces in the years to come
Vaadia, E. (2009) · Frontiers in Neuroscience · doi:10.3389/neuro.01.015.2009 · Cited by 45
Cortical and subcortical mechanisms of brain‐machine interfaces
Marchesotti, S., Martuzzi, R., Schurger, A., Blefari, M. L., Millán, J. D. R., Bleuler, H., & Blanke, O. (2017) · Human Brain Mapping · doi:10.1002/hbm.23566 · Cited by 42
Dangi, S., Gowda, S., Moorman, H. G., Orsborn, A. L., So, K., Shanechi, M. M., & Carmena, J. M. (2014) · Neural Computation · doi:10.1162/neco_a_00632 · Cited by 45
The Neuronal Basis of Long-Term Sensorimotor Learning
Mandelblat-Cerf, Y., Novick, I., Paz, R., Link, Y., Freeman, S., & Vaadia, E. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.4055-10.2011 · Cited by 41
Vučković, A., Wallace, L., & Allan, D. (2014) · Neurology Report · doi:10.1097/npt.0000000000000063 · Cited by 43
Adaptation to a Cortex-Controlled Robot Attached at the Pelvis and Engaged during Locomotion in Rats
Song, W., & Giszter, S. F. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2335-10.2011 · Cited by 43
Quantized Attention-Gated Kernel Reinforcement Learning for Brain–Machine Interface Decoding
Wang, F., Wang, Y., Xu, K., Li, H., Liao, Y., Zhang, Q., Zhang, S., Zheng, X., & Príncipe, J. C. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2493079 · Cited by 39
How learning unfolds in the brain: toward an optimization view
Hennig, J. A., Oby, E. R., Losey, D. M., Batista, A. P., Yu, B. M., & Chase, S. M. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.005 · Cited by 38
Self-citationCortical Plasticity Induced by Spike-Triggered Microstimulation in Primate Somatosensory Cortex
Song, W., Kerr, C. C., Lytton, W. W., & Francis, J. T. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057453 · Cited by 38
Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System
Sorrell, E., Rule, M. E., & O’Leary, T. (2021) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-061720-012348 · Cited by 41
Dynamic range adaptation in primary motor cortical populations
Rasmussen, R. G., Schwartz, A. B., & Chase, S. M. (2017) · eLife · doi:10.7554/elife.21409 · Cited by 42
Self-citationLatent Inputs Improve Estimates of Neural Encoding in Motor Cortex
Chase, S. M., Schwartz, A. B., & Kass, R. E. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2325-10.2010 · Cited by 34
Self-citationLaw, A. J. J., Rivlis, G., & Schieber, M. H. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00373.2013 · Cited by 36
Effects of Soft Drinks on Resting State EEG and Brain–Computer Interface Performance
Meng, J., Mundahl, J., Streitz, T. D., Maile, K., Gulachek, N., He, J., & He, B. (2017) · IEEE Access · doi:10.1109/access.2017.2751069 · Cited by 35
Neural Plasticity in Sensorimotor Brain–Machine Interfaces
Dadarlat, M. C., Canfield, R. A., & Orsborn, A. L. (2023) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-110220-110833 · Cited by 35
Enhanced detection threshold forin vivocortical stimulation produced by Hebbian conditioning
Rebesco, J. M., & Miller, L. E. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/1/016011 · Cited by 36
Combined Adaptiveness of Specific Motor Cortical Ensembles Underlies Learning
Arce‐McShane, F. I., Novick, I., Mandelblat-Cerf, Y., Israel, Z. H., Ghez, C. P., & Vaadia, E. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0076-10.2010 · Cited by 34
Recent advances in bioelectric prostheses
Pasquina, P. F., Perry, B. N., Miller, M. E., Ling, G. S. F., & Tsao, J. W. (2015) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000000132 · Cited by 33
Primary motor cortical discharge during force field adaptation reflects muscle-like dynamics
Cherian, A., Fernandes, H. L., & Miller, L. E. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00109.2012 · Cited by 30
Transfer of information by BMI
Tehovnik, E. J., Woods, L., & Slocum, W. M. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.10.003 · Cited by 32
Decoding Hindlimb Movement for a Brain Machine Interface after a Complete Spinal Transection
Manohar, A., Flint, R. D., Knudsen, E. B., & Moxon, K. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052173 · Cited by 34
Unsupervised adaptation of brain-machine interface decoders
Gürel, T. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00164 · Cited by 37
Neuroplasticity of the Sensorimotor Cortex during Learning
Francis, J. T., & Song, W. (2011) · Neural Plasticity · doi:10.1155/2011/310737 · Cited by 37
Designing Dynamical Properties of Brain–Machine Interfaces to Optimize Task-Specific Performance
Gowda, S., Orsborn, A. L., Overduin, S. A., Moorman, H. G., & Carmena, J. M. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2309673 · Cited by 33
Brain-Computer Interface with Inhibitory Neurons Reveals Subtype-Specific Strategies
Mitani, A., Dong, M., & Komiyama, T. (2017) · Current Biology · doi:10.1016/j.cub.2017.11.035 · Cited by 32
Encoder-Decoder Optimization for Brain-Computer Interfaces
Merel, J., Pianto, D. M., Cunningham, J. P., & Paninski, L. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004288 · Cited by 32
Kalaska, J. F. (2019) · F1000Research · doi:10.12688/f1000research.17161.1 · Cited by 28
Functional Biases in Visual Cortex Neurons with Identified Projections to Higher Cortical Targets
Jarosiewicz, B., Schummers, J., Malik, W. Q., Brown, E. N., & Sur, M. (2012) · Current Biology · doi:10.1016/j.cub.2012.01.011 · Cited by 30
Self-citationNeuroplasticity subserving the operation of brain–machine interfaces
Oweiss, K. G., & Badreldin, I. S. (2015) · Neurobiology of Disease · doi:10.1016/j.nbd.2015.05.001 · Cited by 33
Adaptive tracking of human ECoG network dynamics
Ahmadipour, P., Yang, Y., Chang, E. F., & Shanechi, M. M. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abae42 · Cited by 30
Learning leaves a memory trace in motor cortex
Losey, D. M., Hennig, J. A., Oby, E. R., Golub, M. D., Sadtler, P. T., Quick, K. M., Ryu, S. I., Tyler‐Kabara, E. C., Batista, A. P., Yu, B. M., et al. (2024) · Current Biology · doi:10.1016/j.cub.2024.03.003 · Cited by 36
Self-citationTrial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model
Stavisky, S. D., Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1091-16.2016 · Cited by 26
Temporally Segmented Directionality in the Motor Cortex
Suway, S. B., Orellana, J., McMorland, A. J. C., Fraser, G. W., Liu, Z., Velliste, M., Chase, S. M., Kass, R. E., & Schwartz, A. B. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx133 · Cited by 27
Self-citationImproving scalability in systems neuroscience
Chen, Z. S., & Pesaran, B. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.025 · Cited by 27
Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience
Schroeder, K. E., & Chestek, C. A. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00291 · Cited by 28
Prediction of Reach Goals in Depth and Direction from the Parietal Cortex
Filippini, M., Breveglieri, R., Hadjidimitrakis, K., Bosco, A., & Fattori, P. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.090 · Cited by 29
Cortical magnification plus cortical plasticity equals vision?
Born, R. T., Trott, A., & Hartmann, T. S. (2014) · Vision Research · doi:10.1016/j.visres.2014.10.002 · Cited by 26
Brandman, D. M., Burkhart, M. C., Kelemen, J. N., Franco, B., Harrison, M. T., & Hochberg, L. R. (2018) · Neural Computation · doi:10.1162/neco_a_01129 · Cited by 24
Redundant information encoding in primary motor cortex during natural and prosthetic motor control
So, K., Ganguly, K., Jimenez, J., Gastpar, M., & Carmena, J. M. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0369-1 · Cited by 28
Neural correlates of user learning during long-term BCI training for the Cybathlon competition
Tortora, S., Beraldo, G., Bettella, F., Formaggio, E., Rubega, M., Felice, A. D., Masiero, S., Carli, R., Petrone, N., Menegatti, E., et al. (2022) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-022-01047-x · Cited by 24
Internal models engaged by brain-computer interface control
Golub, M. D., Yu, B. M., & Chase, S. M. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346182 · Cited by 22
Self-citationPun, T. K., Khoshnevis, M., Hosman, T., Wilson, G. H., Kapitonava, A., Kamdar, F. B., Henderson, J. M., Simeral, J. D., Vargas-Irwin, C. E., Harrison, M. T., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06784-4 · Cited by 26
Brandman, D. M., & Hochberg, L. R. (2016) · Wiley eBooks · doi:10.1002/9781118816028.ch9 · Cited by 23
Young, D. R., Willett, F. R., Memberg, W. D., Murphy, B., Rezaii, P. G., Walter, B. L., Sweet, J., Miller, J. P., Shenoy, K. V., Hochberg, L. R., et al. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaf606 · Cited by 22
Learning active sensing strategies using a sensory brain–machine interface
Richardson, A. G., Ghenbot, Y. G., Liu, X., Hao, H., Rinehart, C., DeLuccia, S., Maldonado, S. T., Boyek, G., Zhang, M., Aflatouni, F., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1909953116 · Cited by 20
Clinical neuroscience and neurotechnology: An amazing symbiosis
Cometa, A., Falasconi, A., Biasizzo, M., Carpaneto, J., Horn, A., Mazzoni, A., & Micera, S. (2022) · iScience · doi:10.1016/j.isci.2022.105124 · Cited by 21
Stimulus-driven changes in sensorimotor behavior and neuronal functional connectivity
Rebesco, J. M., & Miller, L. E. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53355-5.00006-3 · Cited by 18
Stochastic Optimal Control as a Theory of Brain-Machine Interface Operation
Lagang, M., & Srinivasan, L. (2012) · Neural Computation · doi:10.1162/neco_a_00394 · Cited by 19
Brain–computer interface control along instructed paths
Sadtler, P. T., Ryu, S. I., Tyler‐Kabara, E. C., Yu, B. M., & Batista, A. P. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/1/016015 · Cited by 17
Concurrent control of a brain–computer interface and natural overt movements
Bashford, L., Wu, J., Sarma, D., Collins, K. L., Rao, R. P. N., Ojemann, J. G., & Mehring, C. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aadf3d · Cited by 17
Simultaneous and independent control of a brain-computer interface and contralateral limb movement
Milovanovic, I. P., Robinson, R., Fetz, E. E., & Moritz, C. T. (2015) · Brain-Computer Interfaces · doi:10.1080/2326263x.2015.1080961 · Cited by 18
Jeon, B. B., Fuchs, T. A. E., Chase, S. M., & Kuhlman, S. J. (2022) · Nature Communications · doi:10.1038/s41467-022-31440-y · Cited by 18
Self-citationAdvancing brain-machine interfaces: moving beyond linear state space models
Rouse, A. G., & Schieber, M. H. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00108 · Cited by 18
Wang, Y., Shé, X., Liao, Y., Li, H., Zhang, Q., Zhang, S., Zheng, X., & Príncipe, J. C. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2500585 · Cited by 18
Inference from populations: going beyond models
Chase, S. M., & Schwartz, A. B. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53355-5.00007-5 · Cited by 16
Self-citationControl of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface
Moorman, H. G., Gowda, S., & Carmena, J. M. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2593696 · Cited by 16
Li, Y., Sun, R., Wang, Y., Li, H., & Zheng, X. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165600 · Cited by 18
Recasting brain-machine interface design from a physical control system perspective
Zhang⋆, Y., & Chase, S. M. (2015) · Journal of Computational Neuroscience · doi:10.1007/s10827-015-0566-4 · Cited by 17
Self-citationBrain-Computer Interfaces and Neurorehabilitation
Carabalona, R., Castiglioni, P., & Gramatica, F. (2009) · Studies in health technology and informatics · doi:10.3233/978-1-60750-018-6-160 · Cited by 15
Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates
Sitaram, R., Sánchez‐Corzo, A., Vargas, G. S., Cortese, A., El‐Deredy, W., Jackson, A., & Fetz, E. E. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0093 · Cited by 20
Systems Neuroscience and Rehabilitation
Kansaku, K., & Cohen, L. G. (2011) · Springer eBooks · doi:10.1007/978-4-431-54008-3 · Cited by 15
Intentional signal in prefrontal cortex generalizes across different sensory modalities
Choi, K., & Torres, E. B. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00505.2013 · Cited by 12
Volitional control of single-electrode high gamma local field potentials by people with paralysis
Milekovic, T., Bacher, D., Sarma, A. A., Simeral, J. D., Saab, J., Pandarinath, C., Yvert, B., Sorice, B. L., Blabe, C. H., Oakley, E. M., et al. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00131.2018 · Cited by 16
BRAIN COMPUTER INTERFACES FOR MEDICAL APPLICATIONS
Talab, D. (2010) · Cited by 13
Oby, E. R., Éthier, C., Bauman, M. J., Perreault, E. J., Ko, J. H., & Miller, L. E. (2010) · Elsevier eBooks · doi:10.1016/b978-0-12-375027-3.00011-9 · Cited by 12
Ito, H., Fujiki, S., Mori, Y., & Kansaku, K. (2020) · Neuroscience Research · doi:10.1016/j.neures.2020.03.008 · Cited by 13
Marathe, A. R., & Taylor, D. M. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046031 · Cited by 13
Legenstein, R. A., Chase, S. M., Schwartz, A. B., & Maass, W. (2009) · PubMed · Cited by 11
Self-citationBrain–Machine Interfaces for Persons with Disabilities
Kansaku, K. (2011) · Springer eBooks · doi:10.1007/978-4-431-54008-3_2 · Cited by 10
Adaptive neuron-to-EMG decoder training for FES neuroprostheses
Éthier, C., Acuna, D. E., Solla, S. A., & Miller, L. E. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/046009 · Cited by 12
Chen, S., Zhang, X., Shen, X., Huang, Y., & Wang, Y. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3105968 · Cited by 12
Learning alters neural activity to simultaneously support memory and actionPreprint
Losey, D. M., Hennig, J. A., Oby, E. R., Golub, M. D., Sadtler, P. T., Quick, K. M., Ryu, S. I., Tyler‐Kabara, E. C., Batista, A. P., Yu, B. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.05.498856 · Cited by 10
Self-citationExtending, changing, and explaining the brain
Chirimuuta, M. (2013) · Biology & Philosophy · doi:10.1007/s10539-013-9366-2 · Cited by 9
Reconfiguring Motor Circuits for a Joint Manual and BCI Task
Lansdell, B., Milovanovic, I. P., Mellema, C., Fetz, E. E., Fairhall, A. L., & Moritz, C. T. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2944347 · Cited by 9
Assistive sensory-motor perturbations influence learned neural representationsPreprint
Rajeswaran, P., Payeur, A., Lajoie, G., & Orsborn, A. L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.20.585972 · Cited by 9
Adaptive frequency-domain filtering for neural signal preprocessing
Bedoyan, E., Reddy, J. W., Kalmykov, A., Cohen‐Karni, T., & Chamanzar, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120429 · Cited by 8
Decoding Algorithms for Brain–Machine Interfaces
Brockmeier, A. J., & Príncipe, J. C. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5227-0_4 · Cited by 8
Liu, Z., & Schieber, M. H. (2020) · eNeuro · doi:10.1523/eneuro.0376-20.2020 · Cited by 8
Selective modulation of cortical population dynamics during neuroprosthetic skill learning
Zippi, E. L., You, A. K., Ganguly, K., & Carmena, J. M. (2022) · Scientific Reports · doi:10.1038/s41598-022-20218-3 · Cited by 10
Awasthi, P., Lin, T.-H., Bae, J., Miller, L. E., & Danziger, Z. C. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac97c3 · Cited by 7
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
Burkhart, M. C. (2019) · HAL · Cited by 7
Brain-Machine Interfaces for Motor Control: A Guide for Neuroscience Clinicians
Martin, A. R., Sankar, T., Lipsman, N., & Lozano, A. M. (2012) · Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · doi:10.1017/s0317167100012622 · Cited by 7
Optimizing the Usability of Brain-Computer Interfaces
Zhang⋆, Y., & Chase, S. M. (2018) · Neural Computation · doi:10.1162/neco_a_01076 · Cited by 7
Self-citationThe impact of task context on predicting finger movements in a brain-machine interface
Mender, M. J., Nason, S. R., Temmar, H., Costello, J. T., Wallace, D. M., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., & Chestek, C. A. (2023) · eLife · doi:10.7554/elife.82598 · Cited by 7
Ahmadipour, P., Sani, O. G., Pesaran, B., & Shanechi, M. M. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad1053 · Cited by 7
System identification of brain-machine interface control using a cursor jump perturbation
Stavisky, S. D., Kao, J. C., Sorokin, J. M., Ryu, S. I., & Shenoy, K. V. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146705 · Cited by 9
Kleih, S. C., Lukito, S., & Kübler, A. (2012) · Ergonomics design and management · doi:10.1201/b13019-12 · Cited by 7
Voluntary control of intracortical oscillations for reconfiguration of network activity
Corlier, J., Valderrama, M., Navarrete, M., Lehongre, K., Hasboun, D., Adam, C., Belaïd, H., Clémenceau, S., Baulac, M., Charpier, S., et al. (2016) · Scientific Reports · doi:10.1038/srep36255 · Cited by 9
Engelhard, B., Darshan, R., Ozeri-Engelhard, N., Israel, Z. H., Werner-Reiss, U., Hansel, D., Bergman, H., & Vaadia, E. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/537613 · Cited by 6
Adaptation and learning as strategies to maximize reward in neurofeedback tasks
Osuna-Orozco, R., Zhao, Y., Stealey, H. M., Lu, H., Contreras-Hernandez, E., & Santacruz, S. R. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1368115 · Cited by 6
Metadata
- Authors
- Jarosiewicz, B., Chase, S. M., Fraser, G. W., Velliste, M., Kass, R. E., & Schwartz, A. B.
- Affiliations
- University of Pittsburgh (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2008-12-09
- Volume
- 105
- Issue
- 49
- Pages
- 19486-19491
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2614787
- OpenAlex
- W2128675085
- Cited by
- 302
- References
- 36
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 15 · 2023: 8 · 2022: 11 · 2021: 9 · 2020: 10 · 2019: 19 · 2018: 19 · 2017: 18 · 2016: 19 · 2015: 29 · 2014: 34 · 2013: 22 · 2012: 24
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01eb005847-04)
- National Institute of Neurological Disorders and Stroke (NS-2-2346)
- National Institute of Biomedical Imaging and Bioengineering (EB005847, R01-EB005847)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- neural learning, brain-computer interface, motor cortex, single-unit recording, behavioral strategies, neural correlates of learning, monkeys
- MeSH terms
- Algorithms, Animals, Association Learning, Electrodes, Implanted, Macaca mulatta, Male, Models, Neurological, Motor Cortex, Neuronal Plasticity, Psychomotor Performance, User-Computer Interface, Neural Networks, Computer, Prostheses and Implants
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review