E. Doukkali
Papers
1
Total Citations
14
H-Index
1
About
E. Doukkali is a researcher in neuromorphic engineering and embedded artificial intelligence, with a focus on spike-based neural computation and hardware implementation. Their major contributions lie in bridging biological neural principles with practical, low-power hardware systems. In their most cited work, "Spike pattern recognition using artificial neuron and spike-timing-dependent plasticity implemented on a multi-core embedded platform" (2017, 14 citations), Doukkali demonstrated how spike-timing-dependent plasticity (STDP)—a key mechanism for learning in biological brains—can be efficiently deployed on multi-core embedded platforms for real-time pattern recognition. This work highlights their ability to translate theoretical neuroscience into energy-efficient, scalable hardware solutions, a critical step toward edge AI and autonomous systems. Doukkali’s research is notable for its interdisciplinary approach, combining computational neuroscience, machine learning, and embedded systems design. Their achievements underscore a commitment to advancing neuromorphic computing, offering a pathway to intelligent devices that learn and adapt with minimal power consumption—a vital contribution as the demand for on-device intelligence grows.
Research Focus
Key Achievements
Top Papers
- 1