Filippo Grassia
Papers
1
Total Citations
14
H-Index
1
About
Filippo Grassia is a researcher at the forefront of neuromorphic engineering, specializing in spike-based neural computation and its hardware implementation. His work focuses on developing energy-efficient, brain-inspired systems that process information through artificial neurons and synapses, bridging the gap between biological neural dynamics and embedded technology. In his highly cited 2017 paper, Grassia demonstrated a pioneering approach to spike pattern recognition by implementing artificial neurons and spike-timing-dependent plasticity (STDP) on a multi-core embedded platform. This contribution is notable for showing how unsupervised learning rules can be realized in real-time, low-power hardware—a critical step toward scalable neuromorphic devices for edge computing and sensory processing. With 14 citations, this work has influenced subsequent research in online learning for spiking neural networks and embedded AI. Grassia’s achievements highlight his role in advancing practical neuromorphic systems, making his research essential reading for students and engineers exploring the intersection of computational neuroscience, machine learning, and hardware design.
Research Focus
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Top Papers
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