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HomeUCLA Brain Research Institute (BRI)  > People  > Research Areas (FARs)  > Computational and Systems Neuroscience
Jonathan Kao

Jonathan Kao, Ph.D.

Biography

Dr. Jonathan Kao is an Assistant Professor in the Department of Electrical Engineering. He received his Ph.D. in 2016 in Electrical Engineering from Stanford University working with Dr. Krishna Shenoy, where he developed algorithms for brain-machine interfaces. He continued his post-doctoral research in the same lab, using statistical and machine learning techniques to analyze the dynamics of neural populations. His lab is interested in computational techniques to elucidate computational mechanisms in neural populations as well as designing algorithms for neural prostheses.

Publications

A selected list of publications:

O’Shea Daniel J, Trautmann Eric, Chandrasekaran Chandramouli, Stavisky Sergey, Kao Jonathan C, Sahani Maneesh, Ryu Stephen, Deisseroth Karl, Shenoy Krishna V   The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces Experimental neurology, 2016; .
Kao Jonathan C, Nuyujukian Paul, Ryu Stephen I, Shenoy Krishna V   A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models IEEE transactions on bio-medical engineering, 2016; .
Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V   Leveraging historical knowledge of neural dynamics to rescue decoder performance as neural channels are lost: “Decoder hysteresis” Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015; 2015: 1061-6.
Kao Jonathan C, Nuyujukian Paul, Ryu Stephen I, Churchland Mark M, Cunningham John P, Shenoy Krishna V   Single-trial dynamics of motor cortex and their applications to brain-machine interfaces Nature communications, 2015; 6: 7759.
Stavisky Sergey D, Kao Jonathan C, Nuyujukian Paul, Ryu Stephen I, Shenoy Krishna V   A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes Journal of neural engineering, 2015; 12(3): 036009.
Even-Chen Nir, Stavisky Sergey D, Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V   Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015; 2015(3): 71-5.
Nuyujukian Paul, Fan Joline M, Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V   A high-performance keyboard neural prosthesis enabled by task optimization IEEE transactions on bio-medical engineering, 2015; 62(1): 21-9.
Nuyujukian Paul, Kao Jonathan C, Fan Joline M, Stavisky Sergey D, Ryu Stephen I, Shenoy Krishna V   Performance sustaining intracortical neural prostheses Journal of neural engineering, 2014; 11(6): 066003.
Stavisky Sergey D, Kao Jonathan C, Nuyujukian Paul, Ryu Stephen I, Shenoy Krishna V   Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014; 2014(1): 3041-4.
Fan Joline M, Nuyujukian Paul, Kao Jonathan C, Chestek Cynthia A, Ryu Stephen I, Shenoy Krishna V   Intention estimation in brain-machine interfaces Journal of neural engineering, 2014; 11(1): 016004.
Kao Jonathan C, Nuyujukian Paul, Stavisky Sergey, Ryu Stephen I, Ganguli Surya, Shenoy Krishna V   Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013; 2013(1): 293-8.
Gilja Vikash, Nuyujukian Paul, Chestek Cindy A, Cunningham John P, Yu Byron M, Fan Joline M, Churchland Mark M, Kaufman Matthew T, Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V   A high-performance neural prosthesis enabled by control algorithm design Nature neuroscience, 2012; 15(12): 1752-7.
Sussillo David, Nuyujukian Paul, Fan Joline M, Kao Jonathan C, Stavisky Sergey D, Ryu Stephen, Shenoy Krishna   A recurrent neural network for closed-loop intracortical brain-machine interface decoders Journal of neural engineering, 2012; 9(2): 026027.

Alicia Izquierdo, Ph.D.

Publications

A selected list of publications:

González VV, Zhang Y, Ashikyan SA, Rickard A, Yassine I, Romero-Sosa JL, Blaisdell AP, Izquierdo A (2024). A special role for anterior cingulate cortex, but not orbitofrontal cortex or basolateral amygdala, in choices involving information. Cerebral Cortex 34(4): bhae135.

Aguirre CG, Woo JH, Romero-Sosa JL, Rivera ZM, Tejada AN, Munier JJ, Perez J, Goldfarb M, Das K, Gomez M, Ye T, Pannu J, Evans K, O’Neill PR, Spigelman I, Soltani A, Izquierdo A (2024). Dissociable contributions of basolateral amygdala and ventrolateral orbitofrontal cortex to flexible learning under uncertainty. The Journal of Neuroscience 44(2): e0622232023.

Ye T, Romero-Sosa JL, Rickard A, Aguirre CG, Wikenheiser AM, Blair HT, Izquierdo A (2023). Theta oscillations in anterior cingulate cortex and orbitofrontal cortex differentially modulate accuracy and speed in flexible reward learning. Oxford Open Neuroscience 2: kvad005.

Hart EE, Blair GJ, O’Dell TJ, Blair HT, Izquierdo A (2020). Chemogenetic modulation and single-photon calcium imaging in anterior cingulate cortex reveal a mechanism for effort-based decisions. The Journal of Neuroscience 40 (29) 5628-5643.

Stolyarova A, Rakhshan M, Hart EE, O’Dell TJ, Peters MAK, Lau H, Soltani A, Izquierdo A (2019). Contributions of anterior cingulate cortex and basolateral amygdala to decision confidence and learning under uncertainty. Nature Communications 10: 4704.

Soltani A and Izquierdo A (2019). Adaptive learning under expected and unexpected uncertainty. Nat Rev Neurosci doi: 10.1038/s41583-019-0180-y.

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