Shaghayegh Gomar

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About

Shaghayegh Gomar’s research lies at the fertile intersection of neuromorphic engineering and bio-inspired robotics, where she designs electronic circuits that mimic the brain’s neural dynamics. Her most influential work focuses on the digital synthesis of biological neuron models, particularly the Hindmarsh-Rose and Adaptive Exponential (AdEx) models, to create low-cost, biomimetic systems. In her 2014 paper on synthesizing a thalamic neuron model, she demonstrated how digital hardware can faithfully reproduce the complex bursting and spiking behaviors of real neurons—a crucial step toward building brain-inspired computing and understanding neurological disorders. Her second highly cited work tackles a core challenge in robotics: efficient locomotion. By implementing a Central Pattern Generator (CPG) based on the AdEx neuron model, she showed how a network of synthetic neurons can generate rhythmic signals to control walking in robots, from hexapods to worm-like designs. With over 10 citations on her foundational neuron synthesis paper and 8 on her CPG work, Gomar’s contributions are shaping how engineers build more adaptive, energy-efficient robots and how neuroscientists test theories of neural computation. Her approach—combining rigorous circuit design with biological plausibility—offers a practical path toward machines that move and think more like living organisms.