Papers
2
Total Citations
21
H-Index
2
About
Farnaz Faramarzi is a pioneering researcher at the intersection of neuromorphic engineering and computational neuroscience, with key contributions to bio-inspired hardware design and vision sensing systems. Her most impactful work introduces a neuromorphic digital circuit that models astrocytic calcium oscillations for neuronal information encoding—a novel approach that bridges glial biology with silicon-based computation. This 2019 paper, with 17 citations, demonstrates how astrocytes’ frequency and amplitude modulation of Ca2+ signals can be efficiently realized in digital circuits, offering a pathway toward more biologically realistic neural networks. More recently, Faramarzi led the development of a 128×128 electronically multi-foveated dynamic vision sensor (EF-DVS) featuring real-time resolution reconfiguration—a breakthrough in event-based vision that mimics the primate retina’s foveated architecture. This 2024 work, already garnering 4 citations, enables adaptive visual processing with unprecedented efficiency for applications in robotics and autonomous systems. Her research uniquely combines cellular-level neurophysiology with VLSI circuit design, positioning her as a rising leader in neuromorphic computing. Faramarzi’s achievements highlight her ability to translate complex biological mechanisms into practical, high-performance hardware, making her work essential reading for students and researchers exploring the future of intelligent sensing and brain-inspired computation.
Research Focus
Key Achievements
Top Papers
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