Neuroelectronics Group

‣ Nanoelectronic devices for neuroinspired/neuromorphic computing

‣Algorithm/device codesign for artificial neural nets

‣Transparent neural interfaces

‣Implantable neurodevices

‣Learning and memory

‣Biophysical modeling

Illustration of our research by Peter Durand

Transparent Neural Interfaces

Calcium imaging has been used to monitor neural populations with single-cell resolution in the brain. Electrophysiological recordings provide high temporal, but limited spatial resolution, because of the geometrical inaccessibility of the brain. We develop transparent and flexible neural interfaces    enabling recording of neural activity with very high resolution from populations and neurons.

Deep 2-photon Imaging and Artifact-free Optogenetics through Transparent Graphene Microelectrode ArraysM. Thunemann, Y. Lu, X. Liu et al, and A. Devor, D. Kuzum,  Nature Communications, 9(1), 2035, 2018.

A compact closed-loop optogenetics system based on artifact free transparent graphene electrodes X. Liu et al and D. KuzumFrontiers in Neuroscience, 12, 32, 2018

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

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

Minimally Invasive Medical Implants

Biophysical Modeling of Learning and Memory

We develop graphene and shape memory based implants that can be translated into medical practice to establish a robust and resilient electrical interface with the human brain. We demonstrated that our technologies allow high resolution detection of cortical waves during epilepsy and can enable deep cortical stimulation from the surface.

3D Expandable Microwire Electrode Arrays Made of Programmable Shape Memory Materials  R. Zhao et al and D. Kuzum,  IEEE IEDM,  2018.

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.

Flexible Neural Electrode Array Based on Porous Graphene for Cortical Microstimulation and Sensing Y. Lu et al, and D. Kuzum, Scientific Reports,  doi:10.1038/srep33526, 2016.

Learning and memory are crucial for high-level cognitive behavior. The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. It has been hypothesized that certain neuron populations in the cortex exhibit coordinated activity with hippocampus during learning and memory retrieval. WE develop biophysical models to investigate mechanisms of hippocampal-cortical memory transfer.

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.

 Computational Analysis of Network Activity and Spatial Reach of Sharp Wave-Ripples   X. Liu et al and D. KuzumPLoS ONE 12(9): e0184542, 2017.

  Transformation of cortex-wide emergent properties during motor learning H. Makino et al, D. Kuzum, and T. Komiyama, Neuron94(4), 880-890, 2017

Nanoelectronic Synaptic Devices for Neuroinspired Computing

As compared to biological systems, today’s programmable computers are 6 to 9 orders of magnitude less efficient. The superior features of the brain, lacking in today’s computational systems, are ultra-high density, low energy consumption, parallelism, robustness and plasticity. We develop programmable nanodevices for  neuroinspired architectures to address inherent limitations of conventional computers.

Neuro-inspired Unsupervised Learning and Pruning with Subquantum CBRAM Arrays Y. Shi et al and D. KuzumNature Communications, 9(1), 5312, 2018.

Artificial optic-neural synapse for colored and color-mixed pattern recognition S. Seo et al, D. Kuzum, H.-S. P. Wong, J.-H. Park L, Nature Communications, 9(1), 5106, 2018.

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

Algorithm-Device Codesign for Efficient Neural Networks

We investigate algorithm-device codesign approaches to develop efficient hardware for neural network models. Our approach harness nonlinear behavior of nanodevices to implement complex neural network operations with high efficiency while providing high accuracy and throughput.

Adaptive Quantization as a Device-algorithm Co-design to Improve Performance of In-memory Unsupervised Learning with SNNs Y. Shi et al, and D. KuzumIEEE Transactions on Electron Devices, 2019

The Impact of Resistance Drift of Phase Change Memory (PCM) Synaptic Devices on Artificial Neural Network Performance S. Oh et al, and D. KuzumIEEE Electron Device Letters, 2019.

A Soft-pruning Method Applied during Training of Spiking Neural Networks for In-memory Computing Applications Y. Shi et al, and D. KuzumFrontiers in Neuroscience,13, 405. 2019.

Research Interests