S. De Fiore
Papers
8
Total Citations
63
H-Index
5
About
S. De Fiore is a researcher whose work sits at the fascinating intersection of bio-inspired robotics, neuromorphic computing, and autonomous navigation. Drawing heavily from biological systems — particularly insect cognition and visual processing in *Drosophila* — De Fiore has made notable contributions to the development of brain-inspired algorithms for real-world robotic applications. Among De Fiore's most influential contributions is the application of Spike Timing Dependent Plasticity (STDP) to robot behavior learning, demonstrated on the TriBot platform (2009, 22 citations), establishing a compelling proof-of-concept for unsupervised spiking neural network learning in mobile robots. Complementing this, a recurring thread through De Fiore's work is the Eye-RIS CNN-based vision system, which served as the backbone for implementing visual homing, target tracking, and fly-inspired orientation models on roving robots. De Fiore's research also ventured into chaos theory-based navigation, implementing weak chaos control on FPGA hardware for real-time perception-action loops. Across these varied methodologies, a unifying theme emerges: translating biological intelligence — from fly visual systems to neural plasticity — into practical, hardware-deployable robotics solutions. With over 60 total citations, De Fiore's work offers valuable contributions to the growing field of neuromorphic and bio-inspired autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1STDP-based behavior learning on the TriBot robot22 citations · 2009
- 2Visual homing: Experimental results on an autonomous robot10 citations · 2007
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- 8Implementation of a CNN-based perceptual framework on a roving robot2 citations · 2008