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Functional network reorganization during learning in a brain-computer interface paradigm
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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.
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“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
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Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., et al. (2012) · Nature · doi:10.1038/nature11076 · Cited by 2,719
Self-citationHigh-performance neuroprosthetic control by an individual with tetraplegia
Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., McMorland, A. J., Velliste, M., Boninger, M. L., & Schwartz, A. B. (2013) · The Lancet · doi:10.1016/s0140-6736(12)61816-9 · Cited by 1,848
Self-citationExplicit and implicit contributions to learning in a sensorimotor adaptation task
Taylor, J. A., Krakauer, J. W., & Ivry, R. B. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3619-13.2014 · Cited by 768
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Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092619-094115 · Cited by 671
Neural constraints on learning
Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryu, S. I., Tyler-Kabara, E. C., Yu, B. M., & Batista, A. P. (2014) · Nature · doi:10.1038/nature13665 · Cited by 713
Self-citationEmergence of a stable cortical map for neuroprosthetic control
Ganguly, K., & Carmena, J. M. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000153 · Cited by 578
Simeral, J. D., Kim, S.-P., Black, M. J., Donoghue, J. P., & Hochberg, L. R. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025027 · Cited by 496
Restoring sensorimotor function through intracortical interfaces: progress and looming challenges
Bensmaia, S. J., & Miller, L. E. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3724 · Cited by 368
Jarosiewicz, B., Sarma, A. A., Bacher, D., Masse, N. Y., Simeral, J. D., Sorice, B., Oakley, E. M., Blabe, C., Pandarinath, C., Gilja, V., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aac7328 · Cited by 340
Self-citationA cortical neural prosthesis for restoring and enhancing memory
Berger, T. W., Hampson, R. E., Song, D. B., Goonawardena, A. V., Marmarelis, V. Z., & Deadwyler, S. A. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/4/046017 · Cited by 316
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Orsborn, A. L., Moorman, H. G., Overduin, S. A., Shanechi, M. M., Dimitrov, D. F., & Carmena, J. M. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.048 · Cited by 280
Competing Roles of Slow Oscillations and Delta Waves in Memory Consolidation versus Forgetting
Kim, J., Gulati, T., & Ganguly, K. (2019) · Cell · doi:10.1016/j.cell.2019.08.040 · Cited by 254
Learning by neural reassociation
Golub, M. D., Sadtler, P. T., Oby, E. R., Quick, K. M., Ryu, S. I., Tyler‐Kabara, E. C., Batista, A. P., Chase, S. M., & Yu, B. M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0095-3 · Cited by 284
Self-citationPerge, J. A., Homer, M. L., Malik, W. Q., Cash, S., Eskandar, E., Friehs, G., Donoghue, J. P., & Hochberg, L. R. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/036004 · Cited by 243
Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework
Ros, T., Baars, B. J., Lanius, R. A., & Vuilleumier, P. O. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.01008 · Cited by 283
Reversible large-scale modification of cortical networks during neuroprosthetic control
Ganguly, K., Dimitrov, D. F., Wallis, J. D., & Carmena, J. M. (2011) · Nature Neuroscience · doi:10.1038/nn.2797 · Cited by 260
Andersen, R. A., Hwang, E. J., & Mulliken, G. H. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.100503 · Cited by 244
New neural activity patterns emerge with long-term learning
Oby, E. R., Golub, M. D., Hennig, J. A., Degenhart, A. D., Tyler-Kabara, E. C., Yu, B. M., Chase, S. M., & Batista, A. P. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820296116 · Cited by 221
“This would result in neural tuning changes that are coordinated across the population (6, 29).”
BackgroundSelf-citationWolpaw, J. R. (2013) · Handbook of clinical neurology · doi:10.1016/b978-0-444-52901-5.00006-x · Cited by 231
Neural Interface Technology for Rehabilitation: Exploiting and Promoting Neuroplasticity
Wang, W., Collinger, J. L., Pérez, M. A., Tyler‐Kabara, E. C., Cohen, L. G., Birbaumer, N., Brose, S. W., Schwartz, A. B., Boninger, M. L., & Weber, D. J. (2009) · Physical Medicine and Rehabilitation Clinics of North America · doi:10.1016/j.pmr.2009.07.003 · Cited by 211
Self-citationCombining Decoder Design and Neural Adaptation in Brain-Machine Interfaces
Shenoy, K. V., & Carmena, J. M. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.038 · Cited by 181
Orsborn, A. L., Dangi, S., Moorman, H. G., & Carmena, J. M. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2185066 · Cited by 174
Intracortical recording stability in human brain–computer interface users
Downey, J. E., Schwed, N., Chase, S. M., Schwartz, A. B., & Collinger, J. L. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab7a0 · Cited by 149
Self-citationThe Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users
Perdikis, S., Tonin, L., Saeedi, S., Schneider, C., & Millán, J. D. R. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2003787 · Cited by 166
Volitional modulation of optically recorded calcium signals during neuroprosthetic learning
Clancy, K. B., Koralek, A. C., Costa, R. M., Feldman, D. E., & Carmena, J. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3712 · Cited by 171
Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses
Gilja, V., Chestek, C. A., Diester, I., Henderson, J. M., Deisseroth, K. A., & Shenoy, K. V. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2107553 · Cited by 160
Rapid calibration of an intracortical brain–computer interface for people with tetraplegia
Brandman, D. M., Hosman, T., Saab, J., Burkhart, M. C., Shanahan, B. E., Ciancibello, J. G., Sarma, A. A., Milstein, D., Vargas-Irwin, C. E., Franco, B., et al. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa9ee7 · Cited by 157
Self-citationLearning to move machines with the mind
Green, A. M., & Kalaska, J. F. (2010) · Trends in Neurosciences · doi:10.1016/j.tins.2010.11.003 · Cited by 157
Recording from the same neurons chronically in motor cortex
Fraser, G. W., & Schwartz, A. B. (2011) · Journal of Neurophysiology · doi:10.1152/jn.01012.2010 · Cited by 150
Self-citationHörzer, G. M., Legenstein, R. A., & Maass, W. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs348 · Cited by 143
Kozai, T. D. Y., Du, Z. J., Gugel, Z. V., Smith, M. A., Chase, S. M., Bodily, L., Caparosa, E. M., Friedlander, R. M., & Cui, X. T. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.12.010 · Cited by 137
Self-citationEvolution of brain-computer interfaces: going beyond classic motor physiology
Leuthardt, E. C., Schalk, G., Roland, J. L., Rouse, A. G., & Moran, D. W. (2009) · Neurosurgical FOCUS · doi:10.3171/2009.4.focus0979 · Cited by 135
Neural Population Dynamics Underlying Motor Learning Transfer
Vyas, S., Even-Chen, N., Stavisky, S. D., Ryu, S. I., Nuyujukian, P., & Shenoy, K. V. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.040 · Cited by 140
Cortical preparatory activity indexes learned motor memories
Sun, X., O’Shea, D. J., Golub, M. D., Trautmann, E. M., Vyas, S., Ryu, S. I., & Shenoy, K. V. (2022) · Nature · doi:10.1038/s41586-021-04329-x · Cited by 137
Movement: How the Brain Communicates with the World
Schwartz, A. B. (2016) · Cell · doi:10.1016/j.cell.2016.02.038 · Cited by 142
Self-citationLegenstein, R. A., Chase, S. M., Schwartz, A. B., & Maass, W. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.4284-09.2010 · Cited by 126
Self-citationParietofrontal integrity determines neural modulation associated with grasping imagery after stroke
Buch, E. R., Shanechi, A. M., Fourkas, A. D., Weber, C., Birbaumer, N., & Cohen, L. G. (2012) · Brain · doi:10.1093/brain/awr331 · Cited by 141
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Homer, M. L., Nurmikko, A. V., Donoghue, J. P., & Hochberg, L. R. (2013) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071910-124640 · Cited by 121
Neural reactivations during sleep determine network credit assignment
Gulati, T., Guo, L., Ramanathan, D. S., Bodepudi, A., & Ganguly, K. (2017) · Nature Neuroscience · doi:10.1038/nn.4601 · Cited by 114
Chase, S. M., Kass, R. E., & Schwartz, A. B. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00371.2011 · Cited by 125
Self-citationBrain-Machine Interfaces beyond Neuroprosthetics
Moxon, K. A., & Foffani, G. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.036 · Cited by 126
Gulati, T., Ramanathan, D., Wong, C. C., & Ganguly, K. (2014) · Nature Neuroscience · doi:10.1038/nn.3759 · Cited by 126
Preparatory activity and the expansive null-space
Churchland, M. M., & Shenoy, K. V. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00796-z · Cited by 133
Real neuroscience in virtual worlds
Dombeck, D. A., & Reiser, M. B. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.10.015 · Cited by 123
Stavisky, S. D., Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.023 · Cited by 113
Emergence of Coordinated Neural Dynamics Underlies Neuroprosthetic Learning and Skillful Control
Athalye, V. R., Ganguly, K., Costa, R. M., & Carmena, J. M. (2017) · Neuron · doi:10.1016/j.neuron.2017.01.016 · Cited by 111
Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces
Dangi, S., Orsborn, A. L., Moorman, H. G., & Carmena, J. M. (2013) · Neural Computation · doi:10.1162/neco_a_00460 · Cited by 103
Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect?
Gerhard, F., Pipa, G., Lima, B., Neuenschwander, S., & Wulfram Gerstner, B. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00004 · Cited by 104
Jarosiewicz, B., Masse, N. Y., Bacher, D., Cash, S. S., Eskandar, E. N., Friehs, G. M., Donoghue, J. P., & Hochberg, L. R. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046012 · Cited by 115
Self-citationReview: Human Intracortical Recording and Neural Decoding for Brain–Computer Interfaces
Brandman, D. M., Cash, S. S., & Hochberg, L. R. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2677443 · Cited by 104
Moran, D. W. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.09.010 · Cited by 104
Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control
Golub, M. D., Chase, S. M., Batista, A. P., & Yu, B. M. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.12.005 · Cited by 102
Self-citationInvasive or Noninvasive: Understanding Brain-Machine Interface Technology [Conversations in BME
Millán, J. D. R., & Carmena, J. M. (2010) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2009.935475 · Cited by 107
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Weber, D. J., Friesen, R., & Miller, L. E. (2012) · Journal of Motor Behavior · doi:10.1080/00222895.2012.735283 · Cited by 98
Decoding three-dimensional reaching movements using electrocorticographic signals in humans
Bundy, D. T., Pahwa, M., Szrama, N. P., & Leuthardt, E. C. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/2/026021 · Cited by 100
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Andersen, R. A., Aflalo, T., & Kellis, S. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902276116 · Cited by 94
Flint, R. D., Scheid, M. R., Wright, Z. A., Solla, S. A., & Slutzky, M. W. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2339-15.2016 · Cited by 93
Intracortical Recording Interfaces: Current Challenges to Chronic Recording Function
Gunasekera, B., Saxena, T. K., Bellamkonda, R. V., & Karumbaiah, L. (2015) · ACS Chemical Neuroscience · doi:10.1021/cn5002864 · Cited by 96
Volitional Control of Neural Activity Relies on the Natural Motor Repertoire
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Brain-Machine Interfaces: A Tale of Two Learners
Perdikis, S., & Millán, J. D. R. (2020) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2019.2958200 · Cited by 92
Hampson, R. E., Song, D. B., Chan, R. H. M., Sweatt, A. J., Riley, M. R., Gerhardt, G. A., Shin, D. C., Marmarelis, V. Z., Berger, T. W., & Deadwyler, S. A. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2189163 · Cited by 90
The Emergence of Single Neurons in Clinical Neurology
Cash, S. S., & Hochberg, L. R. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.058 · Cited by 87
Brain–Computer Interfaces in Neurorecovery and Neurorehabilitation
Young, M. J., Lin, D. J., & Hochberg, L. R. (2021) · Seminars in Neurology · doi:10.1055/s-0041-1725137 · Cited by 93
Information Systems Opportunities in Brain–Machine Interface Decoders
Kao, J. C., Stavisky, S. D., Sussillo, D., Nuyujukian, P., & Shenoy, K. V. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2307357 · Cited by 85
Augmentation-related brain plasticity
Pino, G. D., Maravita, A., Zollo, L., Guglielmelli, E., & Lazzaro, V. D. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00109 · Cited by 83
He, B., Gao, S., Yuan, H., & Wolpaw, J. R. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5227-0_2 · Cited by 86
Toward More Versatile and Intuitive Cortical Brain–Machine Interfaces
Andersen, R. A., Kellis, S., Klaes, C., & Aflalo, T. (2014) · Current Biology · doi:10.1016/j.cub.2014.07.068 · Cited by 83
Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations
Slutzky, M. W. (2018) · The Neuroscientist · doi:10.1177/1073858418775355 · Cited by 86
A dynamical systems view of motor preparation
Shenoy, K. V., Kaufman, M. T., Sahani, M., & Churchland, M. M. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53355-5.00003-8 · Cited by 80
Chirimuuta, M. (2024) · The MIT Press eBooks · doi:10.7551/mitpress/13804.001.0001 · Cited by 82
Xie, H. B., Liu, Y., Zhu, Y., Ding, X., Yang, Y., & Guan, J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1316808111 · Cited by 82
Pohlmeyer, E. A., Mahmoudi, B., Geng, S., Prins, N. W., & Sanchez, J. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087253 · Cited by 79
Decoding arm speed during reaching
Inoue, Y., Mao, H., Suway, S. B., Orellana, J., & Schwartz, A. B. (2018) · Nature Communications · doi:10.1038/s41467-018-07647-3 · Cited by 72
Self-citationDecoding the brain: From neural representations to mechanistic models
Mathis, M. W., Rotondo, A. P., Chang, E. F., Tolias, A. S., & Mathis, A. (2024) · Cell · doi:10.1016/j.cell.2024.08.051 · Cited by 86
Neural basis of sensorimotor learning: modifying internal models
Lalazar, H., & Vaadia, E. (2008) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2008.11.003 · Cited by 84
Single reach plans in dorsal premotor cortex during a two-target task
Dekleva, B. M., Kording, K. P., & Miller, L. E. (2018) · Nature Communications · doi:10.1038/s41467-018-05959-y · Cited by 70
Statistical assessment of the stability of neural movement representations
Stevenson, I. H., Cherian, A., London, B. M., Sachs, N. A., Lindberg, E. W., Reimer, J., Slutzky, M. W., Hatsopoulos, N. G., Miller, L. E., & Kording, K. P. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00626.2010 · Cited by 71
Neuronal Correlates of Memory Formation in Motor Cortex after Adaptation to Force Field
Arce‐McShane, F. I., Novick, I., Mandelblat-Cerf, Y., & Vaadia, E. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.1603-10.2010 · Cited by 70
Neural reinforcement: re-entering and refining neural dynamics leading to desirable outcomes
Athalye, V. R., Carmena, J. M., & Costa, R. M. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.11.023 · Cited by 67
Intention estimation in brain–machine interfaces
Fan, J. M., Nuyujukian, P., Kao, J. C., Chestek, C. A., Ryu, S. I., & Shenoy, K. V. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/1/016004 · Cited by 70
Robust Neuroprosthetic Control from the Stroke Perilesional Cortex
Gulati, T., Won, S. J., Ramanathan, D., Wong, C. C., Bodepudi, A., Swanson, R. A., & Ganguly, K. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5007-14.2015 · Cited by 70
Electrophysiological recordings from behaving animals—going beyond spikes
Chorev, E., Epsztein, J., Houweling, A. R., Lee, A. K., & Brecht, M. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.08.005 · Cited by 69
Learning in Closed-Loop Brain–Machine Interfaces: Modeling and Experimental Validation
Héliot, R., Ganguly, K., Jimenez, J., & Carmena, J. M. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2036931 · Cited by 62
Brain–Machine Interface Control Algorithms
Shanechi, M. M. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2639501 · Cited by 61
Volitional Modulation of Primary Visual Cortex Activity Requires the Basal Ganglia
Neely, R., Koralek, A. C., Athalye, V. R., Costa, R. M., & Carmena, J. M. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.051 · Cited by 62
Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders
Broccard, F. D., Mullen, T. R., Chi, Y. M., Peterson, D., Iversen, J. R., Arnold, M., Kreutz-Delgado, K., Jung, T., Makeig, S., Howard Poizner, A., et al. (2014) · Annals of Biomedical Engineering · doi:10.1007/s10439-014-1032-6 · Cited by 62
Self-recalibrating classifiers for intracortical brain–computer interfaces
Bishop, W. E., Chestek, C. A., Gilja, V., Nuyujukian, P., Foster, J. D., Ryu, S. I., Shenoy, K. V., & Yu, B. M. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/026001 · Cited by 64
Feedback control policies employed by people using intracortical brain–computer interfaces
Willett, F. R., Pandarinath, C., Jarosiewicz, B., Murphy, B. A., Memberg, W. D., Blabe, C. H., Saab, J., Walter, B. L., Sweet, J. A., Miller, J. P., et al. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/14/1/016001 · Cited by 67
Self-citationInternal models for interpreting neural population activity during sensorimotor control
Golub, M. D., Yu, B. M., & Chase, S. M. (2015) · eLife · doi:10.7554/elife.10015 · Cited by 61
Self-citationInterfacing With the Computational Brain
Andrew, J. O., & Fetz, E. E. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2158586 · Cited by 57
Oby, E. R., Éthier, C., & Miller, L. E. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00331.2012 · Cited by 57
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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
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