B. Linares-Barranco
Papers
9
Total Citations
521
H-Index
7
About
B. Linares-Barranco is a pioneering figure in neuromorphic engineering, whose work bridges the gap between biological neural systems and silicon-based computing. His primary research areas include dynamic vision sensors (DVS), spiking neural networks, and biologically plausible learning architectures. Linares-Barranco’s most impactful contribution is the development of a 128×128 asynchronous frame-free DVS (320 citations), which achieved remarkable 1.5% contrast sensitivity, 3 µs latency, and only 4 mW power consumption—a cornerstone for event-driven vision systems. He also introduced SAM, a unified self-adaptive multicompartmental spiking neuron model (77 citations) that integrates working memory into neuromorphic learning, and NADOL (44 citations), a dendrite-driven architecture for spike-based online learning. His work on event-driven sensing for high-speed robotic vision (31 citations) has advanced real-time, energy-efficient visual processing. More recently, Linares-Barranco has explored astrocytic calcium oscillations for neuronal encoding (17 citations) and developed methods for downscaling event data for embedded computer vision (13 citations). His innovations have collectively garnered over 500 citations, establishing him as a leading architect of next-generation neuromorphic systems that mimic the brain’s efficiency and adaptability.
Research Focus
Key Achievements
Top Papers
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- 4Event-driven sensing and processing for high-speed robotic vision31 citations · 2014
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- 6Event Data Downscaling for Embedded Computer Vision13 citations · 2022
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