Jonathan Kao

Jonathan Kao


56-147H Engr. IV

Phone: (310) 983-3068


Our group, the Neural Computation and Engineering Laboratory, aims to understand neural circuit computation and build brain-machine interfaces.
Our research group studies questions at the intersection of neuroscience and computation. In particular, we develop and apply statistical signal processing and machine learning techniques to elucidate how populations of neurons carry out computations in the brain. We also develop experimental and algorithmic techniques for neural engineering applications, including brain-machine interfaces.
  • Keywords: deep learning, brain-machine interfaces, computational and systems neuroscience, neural prostheses, neural signal processing, machine learning, dynamical systems, recurrent neural networks.
  • Jiang X, Saggar H, Ryu SI, Shenoy KV, Kao JC, “Structure in neural activity during observed and executed movements is shared at the neural population level, not in single neurons,” Cell Reports, 32:108006. (2020). (Link:
  • Liang K, Kao JC, “Deep learning neural encoders for motor cortex,” IEEE Transactions on Biomedical Engi- neering, doi:10.1109/TBME.2019.2955722 (2019). (
  • Pandarinath C, Ames KC, Russo A.A., Farshchian A., Miller LE, Dyer EL, Kao, JC, “Latent factors and dy- namics in motor cortex and their application to brain-machine interfaces," Journal of Neuroscience, 38(44):9390- 9401 (2018). (Link:
  • Kao JC*, Nuyujukian P*, Ryu SI, Shenoy KV (2017) A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Transactions on Biomedical Engineering. 64(4):935–945.  (Link:
  • Sussillo D*, Stavisky SD*, Kao JC*, Ryu SI, Shenoy KV (2016) Making brain-machine interfaces robust to future neural variability. Nature Communications. 7:13749.  (Link:
  • Kao JC, Nuyujukian P, Ryu SI, Churchland MM, Cunningham JP, Shenoy KV (2015) Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nature Communications. 6(May):1–12. (Link:
  • Nuyujukian P, Kao JC., Fan JM., Stavisky SD., Ryu SI., Shenoy KV. (2014) Performance sustaining intracortical neural prostheses. Journal of Neural Engineering. 11(6):066003. (Link:
  • Kao JC, Stavisky SD, Sussillo David, Nuyujukian P, Shenoy KV (2014) Information systems opportunities in brain-machine interface decoders. Proceedings of the IEEE. 102(5):666–682. (Link:
  • Gilja V*, Nuyujukian P*, Chestek CA, Cunningham JP, Yu BM, Fan JM, Churchland MM, Kaufman MT, Kao JC, Ryu SI, Shenoy KV (2012) A high-performance neural prosthesis enabled by control algorithm design. Nature Neuroscience. 15(12):1752–7.  (Link:
  • 2016, PhD, Electrical Engineering, Stanford University
  • 2010, MS, Electrical Engineering, Stanford University
  • 2010, BS, Electrical Engineering, Sanford University
  • 2020, NIH Director's New Innovator Award
  • 2020, NSF CAREER Award
  • 2020, Brain & Behavior Research Foundation Young Investigator Grant
  • 2019, Hellman Fellowship
  • 2016, Sammy Kuo Award in Neuroscience, Finalist, Stanford University
  • 2010-2015, NSF Graduate Research Fellowship
  • 2014, BRAIN Best Paper Award, IEEE EMBS, Chicago (Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV)
  • 2006, Hewlett Packard Best Paper Award, ASME IMECE, Chicago (Kao JC, Warren J, Xu J, Attinger D)
  • Neural Networks and Deep Learning (ECE C147/C247; formerly ECE 239AS). Winter '18, '19, '20.
  • Neural Signal Processing (ECE C143A/C243A; formerly ECE 239AS.2). Spring '17, '18, '19, '20.
  • Signals and Systems (ECE 102). Fall '18, '19, '20.
  • Advanced Honors Seminar (ECE 189). Fall '18, '19, '20.