Hegan Chen
Papers
3
Total Citations
108
H-Index
3
About
Hegan Chen is a pioneering researcher in neuromorphic computing and biomimetic circuit design, with a focus on translating biological neural mechanisms into memristive hardware. His work bridges neuroscience and engineering, particularly through the development of self-repairing networks inspired by astrocyte-mediated tripartite synapses. His most cited paper (81 citations) introduces a memristive circuit that emulates the brain’s self-repair capability via endocannabinoid retrograde signaling, with direct applications in robotics. Chen further advances the field by implementing a Caenorhabditis elegans neural mechanism for energy-efficient neuromorphic computing (20 citations), addressing the limitations of von Neumann architectures. His design of a biomimetic memory circuit based on hippocampal mechanisms (7 citations) replicates short-term and long-term memory storage and retrieval, offering a hardware model for cognitive functions. By integrating biological principles—from single-cell repair to whole-organism neural processing—Chen’s work provides scalable, bio-realistic platforms for next-generation computing and autonomous systems. His contributions are pivotal for students and researchers exploring the intersection of neuroscience, memristive technology, and intelligent robotics.
Research Focus
Key Achievements
Top Papers
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