Neuroelectronics Group



24. Performance Prospects of Deeply Scaled Spin-transfer Torque Magnetic Random-access Memory for In-memory Computing Y. Shi, S. Oh, Z. Huang, X. Lu, S. H. Kang, D. KuzumIEEE Electron Device Letters, 2020.

23. Evaluation of Durability of Transparent Graphene Electrodes Fabricated on Different Flexible Substrates for Chronic in vivo Experiments D. Ding, Y. Lu, R. Zhao, X. Liu, C. De-Enamkul, C. Ren, A. Mehrsa, T. Komiyama, D. Kuzum, IEEE Transactions on Biomedical Engineering, 2020.


16) A flexible head fixation system for optical imaging and electrophysiology in awake mice M. Thunemann, P. Machler, N. Thunemann, Y. Lu, X. Liu, D. Kuzum, A. Devor, In Optics and the Brain (pp. JW3A-30). Optical Society of America, 2020.


22.Hippocampal-cortical Memory Trace Transfer and Reactivation through Cell-specific Stimulus and Spontaneous Background Noise Xin Liu, D. Kuzum, Frontiers in Neuroscience, 13, 67, 2019.

21. The Impact of Resistance Drift of Phase Change Memory (PCM) Synaptic Devices on Artificial Neural Network Performance S. Oh, Z. Huang, Y.Shi, D. KuzumIEEE Electron Device Letters, 2019.

20. A Soft-pruning Method Applied during Training of Spiking Neural Networks for In-memory Computing Applications Y. Shi, L. Nguyen, S. Oh, X. Liu, D. KuzumFrontiers in Neuroscience,13, 405. 2019.


19. Adaptive Quantization as a Device-algorithm Co-design to Improve Performance of In-memory Unsupervised Learning with SNNs Y. Shi, Z. Huang, S. Oh, N. Kaslan, J. Song, D. KuzumIEEE Transactions on Electron Devices, 2019.

18. Roadmap on Material-Function Mapping for Photonic-Electronic Hybrid Neural Networks M. Miscuglio, GC Adam, D. Kuzum, VJ Sorger, under review, arXiv:1905.06371.


15) Decoding ECoG High Gamma Power from Cellular Calcium Response Using Transparent Graphene Microelectrodes

X. Liu, C. Ren, Y. Lu, R. Hattori, Y. Shi, R. Zhao, D. Ding, T. Komiyama, D. KuzumIEEE EMBS Conference on Neural Engineering, 2019.

14) Graphene-Based Brain Interfaces for Probing Neural Activity D. Kuzum (invited), ECS Meeting, Dallas TX, 2019


17. Neuro-inspired Unsupervised Learning and Pruning with Subquantum CBRAM Arrays Y. Shi, L. Nguyen, S. Oh, X. Liu, F. Koushan, J. Jameson and D. KuzumNature Communications, 9(1), 5312, 2018. Press release

16. 3D Expandable Microwire Electrode Arrays Made of Programmable Shape Memory Materials  R. Zhao, X. Liu, Y. Lu, C. Ren, A. Mehrsa, T. Komiyama and D. Kuzum,  IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, 2018. Covered by IEEE Spectrum.

15. Deep 2-photon Imaging and Artifact-free Optogenetics through Transparent Graphene Microelectrode ArraysM. Thunemann, Y. Lu, X. Liu, K. KilicM. Desjardins, M. Vandenberghe, S. Sadegh, P. Saisam, P. Cheng, H. Lyu, K. Weldy, S. Djurovic, O. Andreassen, A. Dale, A. Devor, D. Kuzum,  Nature Communications, 9(1), 2035, 2018.

14. Ultra-low Impedance Graphene Microelectrodes with High Optical Transparency for Simultaneous Deep 2-photon Imaging in Transgenic MiceY. Lu, X. Liu, R. Hattori, C. Ren, X. Zhang, T. Komiyama, D. KuzumAdvanced Functional Materials, 1800002, 2018.

13. High-Density Porous Graphene Arrays Enable Detection and Analysis of Propagating Cortical Waves and Spirals X. Liu, Y. Lu, and D. KuzumScientific Reports, 8(1), 17089, 2018.

12. Artificial optic-neural synapse for colored and color-mixed pattern recognition S. Seo, S. Jo, S. Kim, J. Shim, S. Oh, J. Kim, K. Heo, J. Choi, C. Choi, S. Oh, D. Kuzum, H.-S. P. Wong, J.-H. Park L, Nature Communications, 9(1), 5106, 2018.

11. Drift-enhanced Unsupervised Learning of Handwritten Digits in Spiking Neural Network with PCM Synapses S. Oh, Y. Shi, X. Liu, S. Oh, and D. KuzumIEEE Electron Device Letters, 39(11), 1768, 2018.

10. A compact closed-loop optogenetics system based on artifact free transparent graphene electrodes X. Liu, Y. Lu, E. Iseri, Y. Shi and D. KuzumFrontiers in Neuroscience, 12, 32, 2018.

9. Spatiotemporal evolution of focal epileptiform activity from surface and laminar field recordings in cat neocortex H. Bink, M. Sedigh-Sarvestani, I. Fernandez-Lamo, L. Kini, H. Ung, D. Kuzum, F. Vitale, B. Litt, and D. Contreras, Journal of Neurophysiology, 119(6), 2018.

8. Graphene-based Neurotechnologies for Advanced Neural InterfacesY. Lu, X. Liu, D. KuzumCurrent Opinion in Biomedical Engineering, 6, p. 138-147, 2018.

7. Neuroinspired Computing with Resistive-switching Devices (Guest Editorial)D. Kuzum. IEEE Nanotechnology Magazine, 12 (3), 4-4, 2018.


13) Drift-enhanced Unsupervised Learning with PCM Synapses Y. Shi, S. Oh, and D. KuzumDevice Research Conference, 2018.

12) A Compact In vivo Closed-loop Optogenetics System based on Transparent Graphene Microelectrodes X. Liu, Y. Lu, E. Iseri, Y. Shi A. and D. Kuzum, MRS Spring Meeting, 2018

11) Modeling resistive synaptic devices for implementation of unsupervised learning with spiking neural networks, Y. Shi, S. Oh, L. Nguyen, and D. Kuzum, MRS Spring Meeting, 2018.


6. Computational Analysis of Network Activity and Spatial Reach of Sharp Wave-Ripples S. Canakci, M. Toy, A. Inci, X. Liu, D. KuzumPLoS ONE 12(9): e0184542, 2017.  

5. Transformation of cortex-wide emergent properties during motor learning H. Makino, C. Ren, H. Liu, A. N. Kim, N. Kondapaneni, X. Liu, D. Kuzum, and T. Komiyama, Neuron94(4), 880-890, 2017.

4. Synaptic Devices Based on Phase-Change Memory Y. Shi, S. Fong, H.-S. P. Wong and D. Kuzum, in Neuro-inspired Computing Using Resistive Synaptic Devices, pp. 19-51. Springer International Publishing, 2017.

3. Implantable optoelectronic probes for In vivo optogenetics E. Iseri and D. Kuzum, Journal of Neural Engineering,14.3, 031001, 2017.


10) Transparent artifact-free graphene electrodes for compact closed-loop optogenetics systems X. Liu, Y. Lu, E. Iseri, C. Ren, H. Liu, T. Komiyama and D. Kuzum, Proceedings of IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, 2017, pp. 26.1.1-26.1.4.

9) Concurrent in vivo calcium imaging and large-scale electrophysiology using transparent electrode arrays in miceD. Kuzum (invited), Neuroscience 2017, Washington D.C.

8) Laser-printed porous graphene microelectrodes with high charge injection capacity for cortical stimulation, Y. Lu, A.Richardson, T. Lucas, D. Kuzum, Neuroscience 2017, Washington D.C.

7) Engineering Synaptic Devices towards Robust and Energy Efficient Brain Inspired Computing, D. Kuzum (Keynote Speech)IEEE International Conference on Nanotechnology, Pittsburgh PA, July 25-28, 2017.

6) Implementation of Energy Efficient Learning in Neural Networks based on Synaptic Devices, Y. Shi, L. Nguyen, and D. Kuzum (invited), SPIE Optics+Photonics, San Diego CA, 2017.

5) Programming Synaptic Devices for Computational Efficiency and Robustness in Neuromorphic Systems, Y. Shi, A. Malik, and D. Kuzum, MRS Spring Meeting, 2017.

4) Phase Change Materials-based Synaptic Devices for Energy Efficient Implementation of Learning in Hardware, Y. Shi and D. Kuzum, ECS Meeting, 2017.


2Flexible Neural Electrode Array Based on Porous Graphene for Cortical Microstimulation and Sensing Y. Lu, H. Lyu, A. Richardson, T. Lucas, and D. Kuzum, Scientific Reports,  doi:10.1038/srep33526, 2016.

1Bioresorbable Silicon Electronics for Transient Spatio-temporal Mapping of Electrical Activity from the Cerebral Cortex K. Yu*, D. Kuzum*, S. Hwang, B. Kim, H. Juul, B. Litt, J. Rogers et al., Nature Materials, doi:10.1038/nmat4624, 2016. *Equal contribution


3) Transparent microelectrodes eliminating light-induced artifacts for simultaneous optogenetics and electrophysiology, H.  Lyu, X, Liu, D. Kuzum, Neuroscience 2016, San Diego, CA.

2) Graphene Neural Interfaces for Artifact-free Optical Stimulation, H. Lyu, X. Liu, N. Rogers, V. Gilja, D. Kuzum, IEEE Engineering in Medicine and Biology Society Conference (EMBC), Orlando FL, 2016.

1)  Engineering Graphene for Neural Sensing and Stimulation Applications, H. Lyu, Y. Lu, D. Kuzum, ECS Meeting, San Diego CA, 2016.