Paul Nuyujukian

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. doi request reprint Monkey models for brain-machine interfaces: the need for maintaining diversity
    Paul Nuyujukian
    Department of Bioengineering and Stanford Medical School, Stanford University, Stanford, CA 94305, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:1301-5. 2011
  2. doi request reprint A brain machine interface control algorithm designed from a feedback control perspective
    Vikash Gilja
    Dept of Computer Science, Stanford University, Stanford, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 2012:1318-22. 2012
  3. pmc Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex
    Cynthia A Chestek
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA
    J Neural Eng 8:045005. 2011
  4. pmc Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces
    Julie Dethier
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
    J Neural Eng 10:036008. 2013
  5. pmc A high-performance neural prosthesis enabled by control algorithm design
    Vikash Gilja
    Department of Computer Science, Stanford University, Stanford, California, USA
    Nat Neurosci 15:1752-7. 2012
  6. doi request reprint Neural prosthetic systems: current problems and future directions
    Cindy A Chestek
    Dept of Electrical Engineering, Stanford University, Stanford, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:3369-75. 2009
  7. pmc A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces
    John P Cunningham
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 4075, USA
    J Neurophysiol 105:1932-49. 2011
  8. pmc Clinical translation of a high-performance neural prosthesis
    Vikash Gilja
    Department of Neurosurgery, Stanford University, Stanford, California, USA
    Nat Med 21:1142-5. 2015
  9. doi request reprint A framework for relating neural activity to freely moving behavior
    Justin D Foster
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
    Conf Proc IEEE Eng Med Biol Soc 2012:2736-9. 2012
  10. pmc A recurrent neural network for closed-loop intracortical brain-machine interface decoders
    David Sussillo
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 9505, USA
    J Neural Eng 9:026027. 2012

Collaborators

Detail Information

Publications17

  1. doi request reprint Monkey models for brain-machine interfaces: the need for maintaining diversity
    Paul Nuyujukian
    Department of Bioengineering and Stanford Medical School, Stanford University, Stanford, CA 94305, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:1301-5. 2011
    ..Given the physiological diversity of neurological injury and disease, we suggest a need to maintain the current diversity of animal models and to explore additional alternatives, as each mimic different aspects of injury or disease...
  2. doi request reprint A brain machine interface control algorithm designed from a feedback control perspective
    Vikash Gilja
    Dept of Computer Science, Stanford University, Stanford, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 2012:1318-22. 2012
    ....
  3. pmc Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex
    Cynthia A Chestek
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA
    J Neural Eng 8:045005. 2011
    ..This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials...
  4. pmc Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces
    Julie Dethier
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
    J Neural Eng 10:036008. 2013
    ..In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex...
  5. pmc A high-performance neural prosthesis enabled by control algorithm design
    Vikash Gilja
    Department of Computer Science, Stanford University, Stanford, California, USA
    Nat Neurosci 15:1752-7. 2012
    ..Using this algorithm, we demonstrate repeatable high performance for years after implantation in two monkeys, thereby increasing the clinical viability of neural prostheses...
  6. doi request reprint Neural prosthetic systems: current problems and future directions
    Cindy A Chestek
    Dept of Electrical Engineering, Stanford University, Stanford, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:3369-75. 2009
    ..In all, this study suggests that research in cortically-controlled prosthetic systems may require reprioritization to achieve performance that is acceptable for a clinically viable human system...
  7. pmc A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces
    John P Cunningham
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 4075, USA
    J Neurophysiol 105:1932-49. 2011
    ..These findings illustrate the type of discovery made possible by the OPS, and so we hypothesize that this novel testing approach will help in the design of prosthetic systems that will translate well to human patients...
  8. pmc Clinical translation of a high-performance neural prosthesis
    Vikash Gilja
    Department of Neurosurgery, Stanford University, Stanford, California, USA
    Nat Med 21:1142-5. 2015
    ..Measured more than 1 year after implant, the neural cursor-control system showed the highest published performance achieved by a person to date, more than double that of previous pilot clinical trial participants. ..
  9. doi request reprint A framework for relating neural activity to freely moving behavior
    Justin D Foster
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
    Conf Proc IEEE Eng Med Biol Soc 2012:2736-9. 2012
    ..Such encouraging results hint at potential utility of the freely-moving experimental paradigm...
  10. pmc A recurrent neural network for closed-loop intracortical brain-machine interface decoders
    David Sussillo
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 9505, USA
    J Neural Eng 9:026027. 2012
    ..Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications...
  11. pmc Single-trial dynamics of motor cortex and their applications to brain-machine interfaces
    Jonathan C Kao
    Electrical Engineering Department, Stanford University, Stanford, California 94305, USA
    Nat Commun 6:7759. 2015
    ..These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs. ..
  12. doi request reprint Performance sustaining intracortical neural prostheses
    Paul Nuyujukian
    Department of Bioengineering, Stanford University, Stanford, CA School of Medicine, Stanford University, Stanford, CA Department of Neurosurgery, Stanford University, Stanford, CA
    J Neural Eng 11:066003. 2014
    ..The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time...
  13. pmc A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes
    Sergey D Stavisky
    Neurosciences Program, Stanford University, Stanford, CA USA
    J Neural Eng 12:036009. 2015
    ..Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI...
  14. doi request reprint A freely-moving monkey treadmill model
    Justin D Foster
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
    J Neural Eng 11:046020. 2014
    ..We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement...
  15. pmc Autonomous head-mounted electrophysiology systems for freely behaving primates
    Vikash Gilja
    Dept of Computer Science, Stanford University, Stanford, CA 94305, USA
    Curr Opin Neurobiol 20:676-86. 2010
    ..Moving forward, this class of technologies, along with advances in neural signal processing and behavioral monitoring, have the potential to dramatically expand the scope and scale of electrophysiological studies...
  16. pmc Neural population dynamics in human motor cortex during movements in people with ALS
    Chethan Pandarinath
    Department of Neurosurgery, Stanford University, Stanford, United States
    elife 4:e07436. 2015
    ..We find that activity in human motor cortex has similar dynamical structure to that of non-human primates, indicating that human motor cortex contains a similar underlying dynamical system for movement generation. ..
  17. doi request reprint HermesC: low-power wireless neural recording system for freely moving primates
    Cynthia A Chestek
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
    IEEE Trans Neural Syst Rehabil Eng 17:330-8. 2009
    ..The HermesC-INI3 system was used to record and telemeter one channel of broadband neural data at 15.7 kSps from a monkey performing routine daily activities in the home cage...