Ammar Belatreche
Papers
2
Total Citations
417
H-Index
1
About
Ammar Belatreche is a leading researcher in computational neuroscience and neuromorphic engineering, with a primary focus on biologically plausible spiking neural networks (SNNs). His seminal review, "A review of learning in biologically plausible spiking neural networks" (2019), has garnered over 416 citations, establishing itself as a foundational reference for understanding how SNNs can emulate synaptic plasticity and learning rules observed in biological systems. This work critically surveys spike-timing-dependent plasticity (STDP) and other local learning mechanisms, bridging the gap between neuroscience and artificial intelligence. Beyond theoretical contributions, Belatreche applies reinforcement learning to robotics, as demonstrated in his study on hexapod robot trajectory control using Q-learning and SARSA algorithms. His research not only advances the development of energy-efficient, brain-inspired computing but also provides practical frameworks for autonomous robotic systems. Belatreche’s work is instrumental for students and researchers exploring the intersection of neural computation, adaptive robotics, and neuromorphic hardware, offering both comprehensive reviews and actionable algorithms for real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1A review of learning in biologically plausible spiking neural networks416 citations · 2019
- 2