Duygu Kuzum
Electrical and Computer Engineering Department at University of California, San Diego
Neuro-inspired architectures and reconfigurable-adaptive systems are emerging research fields aiming to go beyond capabilities of digital logic and eventually to reach brain-like learning efficiency. In this talk, I will present novel electronic devices for neuro-inspired computing, performing weight updates to implement learning in the hardware. I will discuss several aspects of neuro-inspired computation including energy efficiency, robustness and parallelism. Then I will briefly discuss how neuro-inspired algorithms can be implemented using synaptic devices. In the second part of my talk, I will introduce a new flexible transparent neural probe made of graphene for simultaneous electrophysiology and neuroimaging. Understanding dynamics of neural circuits requires probing them with high spatial and temporal resolution, simultaneously. Graphene-based neurotechologies enable seamless integration of optical and electrical modalities to probe neural circuits with high spatio-temporal resolution.